AIR CONDITIONING SYSTEM, REFRIGERANT AMOUNT ESTIMATION METHOD FOR AIR CONDITIONING SYSTEM, AIR CONDITIONER, AND REFRIGERANT AMOUNT ESTIMATION METHOD FOR AIR CONDITIONER

Description

FIELD

The present invention relates to an air conditioning system, a refrigerant amount estimation method for an air conditioning system, an air conditioner, and a refrigerant amount estimation method for an air conditioner.

BACKGROUND

In recent years, in a multi-room air conditioner having a structure in which a plurality of indoor units are connected to an outdoor unit, various methods for detecting an amount of refrigerant that is filled in a refrigerant circuit have been proposed. Patent Literature 1 discloses a method for determining an amount of refrigerant by using a degree of supercooling of a refrigerant at an outlet of a condenser on the basis of, for example, a refrigerant circuit that is used as a predetermined condition.

In addition, the present inventor discloses, in Patent Literature 2, a method for generating, on the basis of multiple regression analysis, a model for estimating an amount of refrigerant of a refrigerant that remains in a refrigerant circuit by using a feature value of the refrigerant circuit related to the amount of refrigerant and estimating a remaining refrigerant amount by using the model.

CITATION LIST
Patent Literature

Patent Literature 1: Japanese Laid-open Patent Publication No. 2006-23072

Patent Literature 2: Japanese Laid-open Patent Publication No. 2021-156528

SUMMARY
Technical Problem

In the above described estimation model that estimates the remaining refrigerant amount, the remaining refrigerant amount is estimated by using a feature value that has a correlation with the remaining refrigerant amount from among a plurality of feature values that are related to the refrigerant circuit. However, in some cases, these feature values may also have a correlation with an abnormal state, such as, a failure occurring in a compressor, that is other than a decrease in the remaining refrigerant amount of refrigerant caused by a refrigerant leakage. Accordingly, in the case where one of the feature values that has a correlation with the remaining refrigerant amount is changed caused by a factor other than the refrigerant leakage, for example, caused by a failure of a device constituting the refrigerant circuit, an erroneous estimation result may possibly be obtained for the remaining refrigerant amount. In addition, in the case where a feature value that is other than the feature value that has a correlation with the remaining refrigerant amount is changed caused by a factor other than the refrigerant leakage, a feature value that has a correlation with a leakage of a refrigerant and that is seemingly normal may also affected by a factor other than the refrigerant leakage.

Accordingly, the present invention has been conceived in light of the circumstances described above and an object thereof is to provide an air conditioning system, a refrigerant amount estimation method for an air conditioning system, an air conditioner, and a refrigerant amount estimation method for an air conditioner that allow for improvement in accuracy of estimating a remaining refrigerant amount even when a feature value that is used to estimate the remaining refrigerant amount is affected by another problem.

Solution to Problem
Advantageous Effects of Invention

As an aspect of an embodiment, accuracy of estimating a remaining refrigerant amount is improved even when a feature value that is used to estimate the remaining refrigerant amount is affected by another problem.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating one example of an air conditioner according to the present embodiment.

FIG. 2 is a diagram illustrating one example of an outdoor unit and an indoor unit.

FIG. 3 is a block diagram illustrating one example of a control circuit included in the outdoor unit.

FIG. 4 is a Mollier diagram illustrating a state of a change in a refrigerant in the air conditioner.

FIG. 5 is a diagram illustrating one example of a first feature value that is used for first to third cooling purpose estimation models and a second feature value that is used for a cooling time determination model.

FIG. 6 is a diagram illustrating one example of a first feature value that is used for first to third heating purpose estimation models and a second feature value that is used for a heating time determination model.

FIG. 7A is a diagram illustrating one example in which an estimation result obtained by the first cooling purpose estimation model and an estimation result obtained by the second cooling purpose estimation model are not interpolated by a sigmoid curve.

FIG. 7B is a diagram illustrating one example in which the estimation result obtained by the first cooling purpose estimation model and the estimation result obtained by the second cooling purpose estimation model are interpolated by a sigmoid curve.

FIG. 8A is a diagram illustrating one example in which an estimation result obtained by the first heating purpose estimation model and an estimation result obtained by the second heating purpose estimation model are not interpolated by a sigmoid curve.

FIG. 8B is a diagram illustrating one example in which the estimation result obtained by the first heating purpose estimation model and the estimation result obtained by the second heating purpose estimation model are interpolated by a sigmoid curve.

FIG. 9 is a diagram illustrating one example of a distribution method of detection values of the second feature values in the determination model.

FIG. 10 is a diagram illustrating an example of anomaly detection by an outlier.

FIG. 11 is a flowchart illustrating one example of a processing operation performed by the control circuit related to an estimation process.

FIG. 12 is a flowchart illustrating one example of a processing operation performed by the control circuit related to a multiple regression analysis process.

FIG. 13 is a diagram illustrating one example of a failure decision table included in a control unit.

FIG. 14 is a diagram illustrating one example of an air conditioning system according to a second embodiment.

DESCRIPTION OF EMBODIMENTS

Preferred embodiments of an air conditioning system, a refrigerant amount estimation method for an air conditioning system, an air conditioner, and a refrigerant amount estimation method for an air conditioner will be explained in detail with reference to accompanying drawings. Furthermore, the disclosed technology is not limited to the present embodiments. Furthermore, the embodiments described below may also be appropriately modified as long as the embodiments do not conflict with each other.

First Embodiment
<Configuration of Air Conditioner>

FIG. 1 is a diagram illustrating one example of an air conditioner 1 according to the present embodiment. The air conditioner 1 illustrated in FIG. 1 includes a single piece of an outdoor unit 2, and N indoor units 3 (N is a natural number equal to or larger than 2). The outdoor unit 2 is connected to each of the indoor units 3 in a parallel manner via a liquid pipe 4 and a gas pipe 5. Then, a refrigerant circuit 6 in the air conditioner 1 is formed by connecting the outdoor unit 2 and the indoor unit 3 to each other by refrigerant pipes, such as the liquid pipe 4 and the gas pipe 5.

FIG. 2 is a diagram illustrating one example of the outdoor unit 2 and the N indoor units 3. The outdoor unit 2 includes a compressor 11, a four-way valve 12, an outdoor heat exchanger 13, an outdoor unit expansion valve 14, a first shut-off valve 15, a second shut-off valve 16, an accumulator 17, an outdoor unit fan 18, and a control circuit 19. The compressor 11, the four-way valve 12, the outdoor heat exchanger 13, the outdoor unit expansion valve 14, the first shut-off valve 15, the second shut-off valve 16, and the accumulator 17 as described above are connected to each other by each of refrigerant pipes that will be described in detail below, and forms an outdoor side refrigerant circuit that constitutes a part of the refrigerant circuit.

The compressor 11 is, for example, a variable capacity compressor of a pressurized container type in which operation capacity is able to be changed in accordance with drive of a motor (not illustrated) for which a rotation speed is controlled by an inverter. The compressor 11 is connected to a first port 12A of the four-way valve 12 by a discharge pipe 21 at the refrigerant discharge side of the compressor 11. In addition, the compressor 11 is connected to a refrigerant outflow side of the accumulator 17 by a suction pipe 22 at the refrigerant suction side of the compressor 11.

The four-way valve 12 is a valve for changing a flow direction of a refrigerant flowing in the refrigerant circuit 6, and includes the first to fourth ports 12A to 12D. The first port 12A is connected to the refrigerant discharge side of the compressor 11 by the discharge pipe 21. The second port 12B is connected to one of the refrigerant inlet and outlet of the outdoor heat exchanger 13 by an outdoor refrigerant pipe 23. The third port 12C is connected to the refrigerant inflow side of the accumulator 17 by an outdoor refrigerant pipe 26. Then, the fourth port 12D is connected to the second shut-off valve 16 by an outdoor gas pipe 24.

The outdoor heat exchanger 13 performs heat exchange between the refrigerant and outside air that is taken into the interior of the outdoor unit 2 caused by a rotation of the outdoor unit fan 18. The outdoor heat exchanger 13 is connected to the second port 12B of the four-way valve 12 by the outdoor refrigerant pipe 26 at one of the refrigerant inlet and outlet of the outdoor heat exchanger 13. The outdoor heat exchanger 13 is connected to the first shut-off valve 15 by an outdoor liquid pipe 25 at the other of the refrigerant inlet and outlet of the outdoor heat exchanger 13. The outdoor heat exchanger 13 functions as a condenser when the air conditioner 1 performs a cooling operation, and functions as an evaporator when the air conditioner 1 performs a heating operation.

The outdoor unit expansion valve 14 is an electronic expansion valve that is arranged in the outdoor liquid pipe 25 and that is driven by a pulse motor (not illustrated). The outdoor unit expansion valve 14 adjusts an amount of refrigerant flowing into the outdoor heat exchanger 13 or an amount of refrigerant flowing out from the outdoor heat exchanger 13 as a result of the degree of opening of the outdoor unit expansion valve 14 being adjusted in accordance with the number of pulses given to the pulse motor. The degree of opening of the outdoor unit expansion valve 14 is adjusted such that, when the air conditioner 1 performs a heating operation, a degree of superheat of refrigerant at the refrigerant suction side of the compressor 11 reaches a target degree of suction superheat. In addition, the degree of opening of the outdoor unit expansion valve 14 is set to be fully opened when the air conditioner 1 performs a cooling operation.

The accumulator 17 is connected to the third port 12C of the four-way valve 12 by the outdoor refrigerant pipe 26 at the refrigerant inflow side of the accumulator 17. Furthermore, the accumulator 17 is connected to the refrigerant inflow side of the compressor 11 by the suction pipe 22 at the refrigerant outflow side of the accumulator 17. The accumulator 17 separates the refrigerant that has flowed from the outdoor refrigerant pipe 26 into the interior of the accumulator 17 into a gas refrigerant and a liquid refrigerant, and allows only the gas refrigerant to be sucked into the compressor 11.

The outdoor unit fan 18 is made of a resin material and is arranged in the vicinity of the outdoor heat exchanger 13. The outdoor unit fan 18 takes outside air into the interior of the outdoor unit 2 from a suction port (not illustrated) in accordance with a rotation of a fan motor (not illustrated), and discharges the outside air that has been subjected to heat exchange with the refrigerant in the outdoor heat exchanger 13 to the outside of the outdoor unit 2 from a wind outlet (not illustrated).

In addition, a plurality of sensors are arranged in the outdoor unit 2. In the discharge pipe 21, a discharge pressure sensor 31 that detects a discharge pressure that is a pressure of the refrigerant that is discharged from the compressor 11, and a discharge temperature sensor 32 that detects a temperature of the refrigerant that is discharged from the compressor 11, that is, a discharge temperature, are arranged. In the vicinity of a refrigerant inflow port of the accumulator 17 connected to the outdoor refrigerant pipe 26, a suction pressure sensor 33 that detects a suction pressure that is a pressure of the refrigerant that is sucked into the compressor 11, and a suction temperature sensor 34 that detects a temperature of the refrigerant that is sucked into the compressor 11 are arranged.

In the outdoor liquid pipe 25 located between the outdoor heat exchanger 13 and the outdoor unit expansion valve 14, a refrigerant temperature sensor 35 that is used to detect a temperature of the refrigerant that flows into the outdoor heat exchanger 13 or a temperature of the refrigerant that flows out of the outdoor heat exchanger 13 is arranged. Then, in the vicinity of the suction port (not illustrated) of the outdoor unit 2, an outdoor air temperature sensor 36 that detects a temperature of outside air that flows into the interior of the outdoor unit 2, that is, an outdoor air temperature, is arranged.

The control circuit 19 controls the entirety of the air conditioner 1. FIG. 3 is a block diagram illustrating one example of the control circuit 19 included in the outdoor unit 2. The control circuit 19 includes an acquisition unit 41, a communication unit 42, a storage unit 43, a control unit 44, an estimation unit 45, and a determination unit 46. The acquisition unit 41 acquires sensor values of the detection units that are the various kinds of sensors described above. The communication unit 42 is a communication interface for performing communication with a communication unit included in each of the indoor units 3. The storage unit 43 is, for example, a flash memory, and stores therein a control program of the outdoor unit 2, an operating state quantity, such as detection values corresponding to detection signals received from the various kinds of sensors, a drive state of each of the compressor 11 and the outdoor unit fan 18, operation information (for example, including operation and stop information, an operation mode, such as cooling or heating, and the like) that is transmitted from each of the indoor units 3, rated capacity of the outdoor unit 2, requested capacity of each of the indoor units 3, and the like. Furthermore, the storage unit 43 includes an anomaly log storage unit 43A that stores therein an anomaly log that will be described later.

The control unit 44 periodically (for example, every 30 seconds) acquires the detection values that are obtained by the various kinds of sensors via the communication unit 42, and receives, via the communication unit 42, an input of a signal including the operating state quantity that is transmitted from each of the indoor units 3. The control unit 44 performs, on the basis of the various kinds of input information, adjustment of the degree of opening of the outdoor unit expansion valve 14 and drive control of the compressor 11.

The estimation unit 45 includes an estimation model 45A that estimates a refrigerant shortage rate of the refrigerant circuit 6 by using the detection value of a first feature value obtained in the case where an operating state quantity related to an amount of the refrigerant in the refrigerant circuit 6 is indicated by the first feature value. In the present embodiment, for example, a relative amount of refrigerant is used as an amount of refrigerant that remains in the refrigerant circuit 6. Specifically, the estimation model 45A is a model that estimates the refrigerant shortage rate of the refrigerant circuit 6 (indicating an amount of decrease from a defined amount, where 100% indicates that the defined amount of refrigerant is filled. The same applies hereinafter). The estimation model 45A includes a first cooling purpose estimation model 45A1, a second cooling purpose estimation model 45A2, a third cooling purpose estimation model 45A3, a first heating purpose estimation model 45A4, a second heating purpose estimation model 45A5, and a third heating purpose estimation model 45A6. Each of the estimation models will be described in detail later.

The determination unit 46 includes a determination model 46A that determines whether or not the detection value of the first feature value is a detection value that is to be used to estimate the refrigerant shortage rate performed by the estimation unit 45 by using the detection value of a second feature value from among the operating state quantities. The determination model 46A includes a cooling time determination model 46B that is used when the air conditioner 1 performs a cooling operation, and a heating time determination model 46C that is used when the air conditioner 1 performs a heating operation. Each of the determination models will be described in detail later.

As illustrated in FIG. 2, the indoor unit 3 includes an indoor heat exchanger 51, an indoor unit expansion valve 52, a liquid pipe connection portion 53, a gas pipe connection portion 54, and an indoor unit fan 55. The indoor heat exchanger 51, the indoor unit expansion valve 52, the liquid pipe connection portion 53, and the gas pipe connection portion 54 are connected to each other by each of the refrigerant pipes that will be described later, and constitutes an indoor unit refrigerant circuit that forms a part of the refrigerant circuit 6.

The indoor heat exchanger 51 performs heat exchange between the refrigerant and indoor air that is taken into the interior of the indoor unit 3 from a suction port (not illustrated) caused by a rotation of the indoor unit fan 55. The indoor heat exchanger 51 is connected to the liquid pipe connection portion 53 by an indoor liquid pipe 56 at one of the refrigerant inlet and outlet of the indoor heat exchanger 51. In addition, the indoor heat exchanger 51 is connected to the gas pipe connection portion 54 by an indoor gas pipe 57 at the other of the refrigerant inlet and outlet of the indoor heat exchanger 51. The indoor heat exchanger 51 functions as a condenser when the air conditioner 1 performs a heating operation. In contrast, the indoor heat exchanger 51 functions as an evaporator when the air conditioner 1 performs a cooling operation.

The indoor unit expansion valve 52 is arranged in the indoor liquid pipe 56, and is an electronic expansion valve. When the indoor heat exchanger 51 functions as an evaporator, that is, when the indoor unit 3 performs a cooling operation, the degree of opening of the indoor unit expansion valve 52 is adjusted such that the degree of superheat of refrigerant at the refrigerant outlet side (at the side of the gas pipe connection portion 54) of the indoor heat exchanger 51 reaches the target degree of superheat of the refrigerant. In addition, when the indoor heat exchanger 51 functions as a condenser, that is, when the indoor unit 3 performs a heating operation, the degree of opening of the indoor unit expansion valve 52 is adjusted such that the degree of supercooling of the refrigerant at the refrigerant outlet side (at the side of the liquid pipe connection portion 53) of the indoor heat exchanger 51 reaches the target degree of supercooling of the refrigerant. Here, the target degree of superheat of the refrigerant and the target degree of supercooling of the refrigerant are the degree of superheat of refrigerant and the degree of supercooling of the refrigerant that are needed to exhibit sufficient cooling capacity and heating capacity in the indoor unit 3.

The indoor unit fan 55 is made of a resin material and is arranged in the vicinity of the indoor heat exchanger 51. The indoor unit fan 55 takes indoor air into the interior of the indoor unit 3 from a suction port (not illustrated) as a result of being rotated by a fan motor (not illustrated), and discharges the indoor air that has been subjected to heat exchange with the refrigerant in the indoor heat exchanger 51 from a wind outlet (not illustrated).

Various kinds of sensors are arranged in the indoor unit 3. In the indoor liquid pipe 56, a liquid side refrigerant temperature sensor 61 that detects a temperature of the refrigerant that flows into the indoor heat exchanger 51 (heat exchange inlet temperature at the side of the indoor unit at the time of a cooling operation), or a temperature of the refrigerant that flows out of the indoor heat exchanger 51 (heat exchange outlet temperature at the side of the indoor unit at the time of a heating operation) is arranged between the indoor heat exchanger 51 and the indoor unit expansion valve 52. In the indoor gas pipe 57, a gas side temperature sensor 62 that detects a temperature of the refrigerant that flows out of the indoor heat exchanger 51 (heat exchange outlet temperature at the side of the indoor unit at the time of a cooling operation), or a temperature of the refrigerant that flows into the indoor heat exchanger 51 (heat exchange inlet temperature at the side of the indoor unit at the time of a heating operation) is arranged. In the vicinity of the suction port (not illustrated) of the indoor unit 3, a suction temperature sensor 63 that detects a temperature of the indoor air that flows into the interior of the indoor unit 3, that is, a suction temperature, is arranged.

In the following, the flow of the refrigerant in the refrigerant circuit 6 and an operation of each of the units when the air conditioner 1 according to the present embodiment performs an air conditioning operation will be described. In addition, arrows illustrated in FIG. 1 indicate the flow of the refrigerant at the time of a heating operation.

When the air conditioner 1 performs a heating operation, the four-way valve 12 is switched such that the first port 12A and the fourth port 12D communicate with each other and the second port 12B and the third port 12C communicate with each other. Accordingly, the refrigerant circuit 6 enters a heating cycle in which each of the indoor heat exchangers 51 functions as a condenser, and the outdoor heat exchanger 13 functions as an evaporator. In addition, for convenience of description, the flow of the refrigerant at the time of a heating operation is indicated by solid arrows illustrated in FIG. 2.

If the compressor 11 is driven when the refrigerant circuit 6 is in the state described above, the refrigerant that has been discharged from the compressor 11 flows through the discharge pipe 21 into the four-way valve 12, flows through the outdoor gas pipe 24 from the four-way valve 12, and flows into the gas pipe 5 via the second shut-off valve 16. The refrigerant that flows through the gas pipe 5 is branched off and flows into each of the indoor unit 3 via each of the gas pipe connection portions 54. The refrigerant that has flowed into each of the indoor units 3 flows through each of the indoor gas pipes 57 and flows into each of the indoor heat exchangers 51. The refrigerant that has flowed into each of the indoor heat exchangers 51 is subjected to heat exchange with the indoor air that is taken into the interior of each of the indoor units 3 as a result of a rotation of each of the indoor unit fans 55, and is then condensed. In other words, each of the indoor heat exchangers 51 functions as a condenser, and the indoor air that is heated by the refrigerant in each of the indoor heat exchangers 51 is blown into a room from a wind outlet (not illustrated), so that the room in which each of the indoor units 3 is installed is heated.

The refrigerant that has flowed into each of the indoor liquid pipes 56 from the respective indoor heat exchangers 51 is depressurized by passing through the respective indoor unit expansion valves 52 for which the degree of opening is adjusted such that the degree of supercooling of the refrigerant at the refrigerant outlet side of each of the indoor heat exchangers 51 reaches the target degree of supercooling of the refrigerant. Here, the target degree of supercooling of the refrigerant is defined on the basis of the cooling capacity that is needed in each of the indoor units 3.

The refrigerant that has been depressurized by each of the indoor unit expansion valves 52 flows out to the liquid pipe 4 from each of the indoor liquid pipes 56 via each of the liquid pipe connection portions 53. The refrigerants that are joined at the liquid pipe 4 flow into the outdoor unit 2 via the first shut-off valve 15. The refrigerant that has flowed into the first shut-off valve 15 of the outdoor unit 2 flows through the outdoor liquid pipe 25 and depressurized by passing through the outdoor unit expansion valve 14. The refrigerant that has been depressurized by the outdoor unit expansion valve 14 flows through the outdoor liquid pipe 25 into the outdoor heat exchanger 13, is subjected to heat exchange with the outside air that has flowed into from the suction port (not illustrated) of the outdoor unit 2 as a result of a rotation of the outdoor unit fan 18, and is then evaporated. The refrigerant that has flowed out to the outdoor refrigerant pipe 26 from the outdoor heat exchanger 13 flows into the four-way valve 12, the outdoor refrigerant pipe 26, the accumulator 17, and the suction pipe 22 in this order, is sucked into the compressor 11 and is then compressed again, and flows out to the outdoor gas pipe 24 by way of the first port 12A and the fourth port 12D of the four-way valve 12.

Furthermore, when the air conditioner 1 performs a cooling operation, the four-way valve 12 is switched such that the first port 12A and the second port 12B communicate with each other and the third port 12C and the fourth port 12D communicate with each other. Accordingly, the refrigerant circuit 6 enters a cooling cycle in which each of the indoor heat exchangers 51 functions as an evaporator and the outdoor heat exchanger 13 functions as a condenser. In addition, for convenience of description, the flow of the refrigerant at the time of a cooling operation is indicated by dashed arrows illustrated in FIG. 2.

The refrigerant that has flowed from the outdoor heat exchanger 13 into the outdoor liquid pipe 25 is depressurized by passing through the outdoor unit expansion valve 14 in which the degree of opening is fully opened. The refrigerant that has been depressurized by the outdoor unit expansion valve 14 flows through the liquid pipe 4 via the first shut-off valve 15 and is branched off and flows into each of the indoor units 3. The refrigerant that has flowed into each of the indoor units 3 flows through the indoor liquid pipe 56 by way of each of the liquid pipe connection portions 53, and is depressurized by passing through the indoor unit expansion valve 52 in which the degree of opening is adjusted such that the degree of supercooling of the refrigerant at the refrigerant outlet of the indoor heat exchanger 51 reaches the target degree of supercooling of the refrigerant. The refrigerant that has been depressurized by the indoor unit expansion valve 52 flows through the indoor liquid pipe 56 into the indoor heat exchanger 51, is subjected to heat exchange with the indoor air that has flowed into from the suction port (not illustrated) of the indoor unit 3 as a result of a rotation of the indoor unit fan 55, and is then evaporated. In other words, each of the indoor heat exchangers 51 functions as an evaporator, and the indoor air that is cooled by the refrigerant in each of the indoor heat exchangers 51 is blown into the room from a wind outlet (not illustrated), so that the room in which each of the indoor units 3 is installed is cooled.

The refrigerant that flows into the gas pipe 5 from the indoor heat exchanger 51 via the gas pipe connection portion 54 flows through the outdoor gas pipe 24 via the second shut-off valve 16 of the outdoor unit 2, and flows into the fourth port 12D of the four-way valve 12. The refrigerant that has flowed into the fourth port 12D of the four-way valve 12 flows into the refrigerant inflow side of the accumulator 17 from the third port 12C. The refrigerant that has flowed from the refrigerant inflow side of the accumulator 17 flows in via the suction pipe 22, is sucked by the compressor 11, and is then compressed again.

The acquisition unit 41 included in the control circuit 19 acquires sensor values of the discharge pressure sensor 31, the discharge temperature sensor 32, the suction pressure sensor 33, the suction temperature sensor 63, the refrigerant temperature sensor 35, and the outdoor air temperature sensor 36 that are included in the outdoor unit 2. Furthermore, the acquisition unit 41 acquires sensor values of the liquid side refrigerant temperature sensor 61, the gas side temperature sensor 62, and the suction temperature sensor 63 that are included in each of the indoor units 3.

FIG. 4 is a Mollier diagram illustrating a cooling cycle of the air conditioner 1. At the time of a cooling operation performed by the air conditioner 1, the outdoor heat exchanger 13 functions as a condenser, and the indoor heat exchanger 51 functions as an evaporator. In addition, at the time of a heating operation performed by the air conditioner 1, the outdoor heat exchanger 13 functions as an evaporator, and the indoor heat exchanger 51 functions as a condenser.

The compressor 11 compresses a low-temperature and low-pressure gas refrigerant that flows in from the evaporator, and discharges a high-temperature and high-pressure gas refrigerant (a refrigerant that enters the state at a point B in FIG. 4). Moreover, the temperature of the gas refrigerant that is discharged from the compressor 11 is a discharge temperature, and the discharge temperature is detected by the discharge temperature sensor 32.

The condenser performs heat exchange between the high-temperature and high-pressure gas refrigerant received from the compressor 11 and air, and then condenses the gas refrigerant. At this time, in the condenser, after the entirety of the gas refrigerant turns into a liquid refrigerant as a result of a change in latent heat, the temperature of the liquid refrigerant is decreased as a result of a change in sensible heat, so that the refrigerant enters in a supercooled state (a state at a point C in FIG. 4). Moreover, the temperature at which the gas refrigerant is changed to the liquid refrigerant caused by the change in latent heat is a high pressure saturation temperature, and the temperature of the refrigerant in the supercooled state at an outlet of the condenser is the temperature at the heat exchange outlet. The high pressure saturation temperature is a temperature that corresponds to a pressure value (a pressure value P2 represented by “HPS” in FIG. 4) that has been detected by the discharge pressure sensor 31. The heat exchange outlet temperature is the temperature of the refrigerant that flows through the outdoor liquid pipe 25 and is detected by the refrigerant temperature sensor 35.

The expansion valve depressurizes the low-temperature and high-pressure refrigerant that has flowed from the condenser, so that a gas-liquid two-phase refrigerant in which gas and liquid are mixed is obtained (a refrigerant that enters the state at a point D in FIG. 4).

The evaporator performs heat exchange between the gas-liquid two-phase refrigerant that has flowed into the evaporator and air, and then evaporates the refrigerant. At this time, in the evaporator, after the entirety of the gas-liquid two-phase refrigerant turns into a gas refrigerant as a result of a change in latent heat, the temperature of the gas refrigerant increases as a result of a change in latent heat, so that the refrigerant enters in a superheated state (a state at a point A in FIG. 4) and is sucked into the compressor 11. Moreover, the temperature at which the liquid refrigerant is changed to the gas refrigerant as the result of the change in latent heat is a low pressure saturation temperature. The low pressure saturation temperature is a temperature that corresponds to a pressure value (a pressure value P1 represented by “LPS” in FIG. 4) that has been detected by the suction pressure sensor 33. In addition, the temperature of the refrigerant that is superheated by the evaporator and sucked into the compressor 11 is a suction temperature. The suction temperature is detected by the suction temperature sensor 34.

In addition, the degree of supercooling of the refrigerant that is in the supercooled state at the time of the refrigerant that flows out from the condenser is able to be calculated by substacting the temperature (the heat exchange outlet temperature described above) of the refrigerant at a refrigerant outlet of the heat exchanger that functions as a condenser from the high pressure saturation temperature. Furthermore, the degree of suction superheat of the refrigerant that is in the superheated state at the time of the refrigerant that flows out from the evaporator is able to be calculated by subtracting the suction temperature from the low pressure saturation temperature.

FIG. 5 is a diagram illustrating one example of the first feature value that is used for the first to the third cooling purpose estimation models 45A1, 45A2, and 45A3 and the second feature value that is used for the cooling time determination model 46B. The first feature value is present as an operating state quantity that is used for the Examples of the first feature value estimation model 45A. that is used for the first to the third cooling purpose estimation models 45A1, 45A2, and 45A3 include a rotation speed of the compressor 11, a high pressure saturation temperature, a suction temperature, a low pressure refrigerant temperature, a degree of supercooling of a refrigerant (outdoor heat exchange subcool), and an outdoor air temperature. The rotation speed of the compressor 11 is detected by a rotation speed sensor (not illustrated) of the compressor 11. The high pressure saturation temperature is a value that is obtained by converting the pressure value that has been detected by the discharge pressure sensor 31 to a temperature. The suction temperature is detected by the suction temperature sensor 34. The low pressure refrigerant temperature is a temperature of the refrigerant that is superheated by the evaporator and that is then sucked into the compressor 11. The degree of supercooling of the refrigerant is a value that has been calculated using an expression indicated by, for example, (the high pressure saturation temperature−the outdoor heat exchange outlet temperature). The outdoor air temperature is detected by the outdoor air temperature sensor 36. The outdoor heat exchange outlet temperature is detected by the refrigerant temperature sensor 35. Moreover, for example, an operating state quantity including the first feature value that is used for the first to the third cooling purpose estimation models 45A1, 45A2, and 45A3 is periodically detected by a detection unit, such as the rotation speed sensor, the discharge pressure sensor 31, the suction temperature sensor 34, the outdoor air temperature sensor 36, or the refrigerant temperature sensor 35. In addition, in the case where the air conditioner 1 is being operated, the control unit 44 instructs the detection unit to periodically (for example, every 10 minutes) acquire the operating state quantity. The detection unit that has received the instruction detects the operating state quantity from various kinds of sensors that are provided in the air conditioner 1. Information on acquisition time is also added to the operating state quantity that has been periodically acquired.

FIG. 6 is a diagram illustrating one example of the first feature value that is used for the first to the third heating purpose estimation models 45A4, 45A5, and 45A6, and the second feature value that is used for the heating time determination model 46C. Examples of the first feature value that is used for the first to the third heating purpose estimation models 45A4, 45A5, and 45A6 include the degree of opening of the outdoor unit expansion valve 14, the rotation speed of the compressor 11, the degree of suction superheat, and the outdoor air temperature. The degree of opening of the outdoor unit expansion valve 14 is the number of pulses that the control unit 44 gives to a stepping motor (not illustrated) of the outdoor unit expansion valve 14. The rotation speed of the compressor 11 is detected by the rotation speed sensor (not illustrated) of the compressor 11. The degree of suction superheat is a value that has been calculated using an expression indicated by, for example, (the suction temperature−the low pressure saturation temperature). The outdoor air temperature is detected by the outdoor air temperature sensor 36. The suction temperature is detected by the suction temperature sensor 34, and the low pressure saturation temperature is a value that is obtained by converting the pressure value that has been detected by the suction pressure sensor 33 to a temperature. Moreover, for example, the operating state quantity including the first feature value that is used for the first to the third heating purpose estimation models 45A4, 45A5, and 45A6 is periodically detected by the detection unit, such as the rotation speed sensor, the suction temperature sensor 34, or the outdoor air temperature sensor 36.

The second feature value is present as an operating state quantity that is used for the determination model 46A. the second feature value that is used to generate the determination model 46A is a value that is obtained when, for example, the refrigerant circuit 6 is implemented on a computer, numerical analysis is performed (hereinafter, performing the numerical analysis is sometimes referred to as performing a simulation), an operation of the refrigerant circuit 6 is normal, and only a remaining refrigerant amount is changed. Moreover, the second feature value that is used to generate the determination model 46A will be referred to as a simulation value (sometimes simply referred to as a “value”). The second feature value includes at least a single piece of the operating state quantity that is included in the first feature value, and includes at least a single piece of the operating state quantity that is not included in the first feature value. As will be described later, the generated determination model 46A is applied to a value of the second feature value (hereinafter, also referred to as a detection value of the second feature value) that is detected by the detection unit. The determination model 46A calculates an outlier of the detection value of the second feature value. The determination unit 46 that will be described later determines, on the basis of the value of the outlier, whether or not the detection value of the first feature value that is acquired by the detection unit at the same time as the detection value of the second feature value is a detection value that is to be used to estimate the refrigerant shortage rate by the estimation unit 45.

The second feature value is detected by the detection unit at the same timing as the first feature value. Specifically, the second feature value is included in the operating state quantity that is instructed by the control unit 44 to be periodically acquired by the detection unit (for example, every 10 minutes). Examples of the second feature value that is used for the cooling time determination model 46B includes, as illustrated in FIG. 5, the rotation speed of the compressor 11, the high pressure saturation temperature, the suction temperature, the low pressure refrigerant temperature, the outdoor air temperature, the discharge pressure, and the heat exchange outlet temperature. The rotation speed of the compressor 11 is detected by the rotation speed sensor (not illustrated) of the compressor 11. The high pressure saturation temperature is a value that is obtained by converting the pressure value that has been detected by the discharge pressure sensor 31 to a temperature. The suction temperature is detected by the suction temperature sensor 34. The low pressure refrigerant temperature is the temperature of the refrigerant that is superheated by the evaporator and that is then sucked into the compressor 11. The outdoor air temperature is detected by the outdoor air temperature sensor 36. The discharge pressure is a pressure value that has been detected by the discharge pressure sensor 31. The heat exchange outlet temperature is detected by the refrigerant temperature sensor 35.

In addition, examples of the second feature value that is used for the heating time determination model 46C include, as illustrated in FIG. 6, the outdoor unit expansion valve 14, the rotation speed of the compressor 11, the outdoor air temperature, the discharge temperature, the suction temperature, the low pressure saturation temperature, and the suction pressure (LPS). The degree of opening of the outdoor unit expansion valve 14 is detected by a sensor (not illustrated). The rotation speed of the compressor 11 is detected by the rotation speed sensor (not illustrated) of the compressor 11. The outdoor air temperature is detected by the outdoor air temperature sensor 36. The discharge temperature is detected by the discharge temperature sensor 32. The suction temperature is detected by the suction temperature sensor 34. The low pressure saturation temperature is a value that is obtained by converting the pressure value that has been detected by the suction pressure sensor 33 to a temperature. The suction pressure is the pressure value that has been detected by the suction pressure sensor 33. Moreover, for example, the operating state quantity including the second feature value that is used for the heating time determination model 46C is periodically detected by the detection unit, such as the rotation speed sensor, the suction temperature sensor 34, the outdoor air temperature sensor 36, or the suction pressure sensor 33.

Examples of the second feature value that is commonly used in both of the cooling time determination model 46B and the heating time determination model 46C include the rotation speed of the compressor 11 and the suction temperature that are the operating state quantity at the side of the outdoor unit 2.

In addition, examples of the second feature value that is commonly used in both of the cooling time determination model 46B and the heating time determination model 46C include the operating state quantity at the side of the indoor unit 3 including, for example, a heat exchange inlet temperature at the indoor unit side (at the time of a cooling operation: detected by the liquid side refrigerant temperature sensor 61, and at the time of a heating operation: detected by the gas side temperature sensor 62), a heat exchange outlet temperature at the indoor unit side (at the time of a cooling operation: detected by the gas side temperature sensor 62, and at the time of a heating operation: detected by the liquid side refrigerant temperature sensor 61), and the degree of opening of the indoor unit expansion valve 52. Moreover, as the second feature value at the side of the indoor unit 3, for example, the heat exchange inlet temperature at the indoor unit side, the heat exchange outlet temperature at the indoor unit side, and the degree of opening of the indoor unit expansion valve 52 are exemplified; however, these are the feature values that are able to be acquired even when each of the indoor units 3 has a different type, such as a duct type or ceiling cassette type.

The estimation model 45A is generated by using a detection value of the first feature value. The estimation unit 45 estimates a refrigerant shortage rate of the refrigerant circuit 6 by applying, to the estimation model 45A, the detection value of the first feature value acquired at a timing that is different from a timing at which the estimation model 45A is generated.

The estimation model 45A is generated by a multiple regression analysis method that is a kind of a regression analysis method by using an arbitrary operating state quantity (detection value of the first feature value) from among a plurality of operating state quantities. In the multiple regression analysis method, the estimation model 45A is generated by selecting a regression equation, in which a P value (a value that indicates a degree of influence of the operating state quantity exerted on the accuracy of the generated estimation model (predetermined weight parameter)) is the smallest value and a correction value R2 (a value that indicates the accuracy of the generated estimation model 45A) is the largest value in a range of 0.9 to 1.0, from among regression equations that are obtained from a plurality of simulation results (results obtained by reproducing the refrigerant circuit 6 by a numerical calculation and calculating values of the operating state quantity with respect to the remaining amount of refrigerant). Here, the P value and the correction value R2 are values that are related to the accuracy of the estimation model 45A at the time of generation of the estimation model 45A based on the multiple regression analysis method, and the accuracy of the generated estimation model 45A is increased as the P value is smaller and the correction value R2 approaches 1.0. As a result, in the case where the refrigerant shortage rate at the time of a cooling operation is in a range of 0 to 30%, for example, the operating state quantities, such as the degree of supercooling of the refrigerant, the outdoor air temperature, the high pressure saturation temperature, and the rotation speed of the compressor 11, are used as the first feature value. In the case where the refrigerant shortage rate at the time of a cooling operation is in a range of 40 to 70%, for example, the operating state quantity, such as the suction temperature, the outdoor air temperature, and the rotation speed of the compressor 11, is used as the first feature value. In the case where the refrigerant shortage rate at the time of a heating operation is in a range of 0 to 20%, for example, as the operating state quantity, the degree of opening of the outdoor unit expansion valve 14 is used as the feature value. Moreover, in the case where the refrigerant shortage rate at the time of a heating operation is in a range of 30% to 70%, for example, the operating state quantities, such as the degree of suction superheat (the suction temperature−the low pressure saturation temperature), the outdoor air temperature, the rotation speed of the compressor 11, and the outdoor unit expansion valve 14, are used as the first feature value.

The estimation model 45A includes, as described above, the first cooling purpose estimation model 45A1, the second cooling purpose estimation model 45A2, the third cooling purpose estimation model 45A3, the first heating purpose estimation model 45A4, the second heating purpose estimation model 45A5, and the third heating purpose estimation model 45A6. In the present embodiment, each of the estimation models is generated by using a simulation result that will be described later, and is stored in the estimation unit 45 included in the control circuit 19 in the air conditioner 1 in advance.

The first cooling purpose estimation model 45A1 is the estimation model 45A that is effective in the case where the refrigerant shortage rate is in a range of 0% to 30% (a first range), and is a first regression equation that is able to estimate the refrigerant shortage rate with high accuracy. The first regression equation is, for example, (α1×the degree of supercooling of the refrigerant)+(α2×the outdoor air temperature)+(α3×the high pressure saturation temperature)+(α4×the rotation speed of the compressor 11)+α5. It is assumed that the coefficients α1 to α5 are determined when the estimation models are generated. The estimation unit 45 calculates the refrigerant shortage rate of the refrigerant circuit 6 at the present time by substituting, into the first regression equation, the degree of supercooling of the refrigerant, the outdoor air temperature, the high pressure saturation temperature, and the rotation speed of the compressor 11 at the current time that are acquired by the acquisition unit 41. Moreover, the reason for substituting the degree of supercooling of the refrigerant, the outdoor air temperature, the high pressure saturation temperature, and the rotation speed of the compressor 11 is to use the first feature value that is used to generate the first cooling purpose estimation model 45A1. The degree of supercooling of the refrigerant is able to be calculated using an expression indicated by, for example, (the high pressure saturation temperature−the heat exchange outlet temperature). The outdoor air temperature is detected by the outdoor air temperature sensor 36. The high pressure saturation temperature is a value obtained by converting the pressure value that has been detected by the discharge pressure sensor 31 to a temperature. The rotation speed of the compressor 11 is detected by the rotation speed sensor (not illustrated) of the compressor 11.

The second cooling purpose estimation model 45A2 is the estimation model 45A that is effective in the case where the refrigerant shortage rate is in a range of 40% to 70% (a second range), and is a second regression equation that is able to estimate the refrigerant shortage rate with high accuracy. The second regression equation is, for example, (α11×the suction temperature)+(α12×the outdoor air temperature)+(α13×the rotation speed of the compressor 11)+α14. It is assumed that the coefficients α11 to α14 are determined when the estimation models are generated. The estimation unit 45 calculates the refrigerant shortage rate of the refrigerant circuit 6 at the present time by substituting, into the second regression equation, the suction temperature, the outdoor air temperature, and the rotation speed of the compressor 11 at the current time that are acquired by the acquisition unit 41. Moreover, the reason for substituting the suction temperature, the outdoor air temperature, and the rotation speed of the compressor 11 is to use the feature value that has been used to generate the second cooling purpose estimation model 45A2. The suction temperature is detected by the suction temperature sensor 34. The outdoor air temperature is detected by the outdoor air temperature sensor 36. The rotation speed of the compressor 11 is detected by the rotation speed sensor (not illustrated) of the compressor 11.

Incidentally, as described above, the refrigerant shortage rate that is able to be obtained by the first regression equation is in a range of 0% to 30%, and the refrigerant shortage rate that is able to be obtained by the second regression equation is in a range of 40% to 70%. In this case, in the case where the refrigerant shortage rate is in a range of 30% to 40%, if the first regression equation is used, the refrigerant shortage rate is calculated as 30%, whereas, if the second regression equation is used, the refrigerant shortage rate is calculated as 40%. In other words, in the case where the refrigerant shortage rate is in a range of 30% to 40%, both of the degree of supercooling of the refrigerant exhibiting a high contribution rate when the refrigerant shortage rate is equal to or less than 30%, and the suction temperature exhibiting a high contribution rate when the refrigerant shortage rate is equal to or larger than 40% are less likely to be changed, so that it is not possible to generate an effective estimation model. Therefore, if the first regression equation or the second regression equation is used, the refrigerant shortage rate largely differs depending on which model is used as illustrated in FIG. 7A.

The third cooling purpose estimation model 45A3 is a cooling time refrigerant shortage rate calculation formula that is able to cover the refrigerant shortage rate in a range of 0% to 70% that includes a range in which it is not able to estimate the refrigerant shortage rate by using any of the first regression equation and the second regression equation as described above. As illustrated in FIG. 7B, the cooling time refrigerant shortage rate calculation formula continuously connects a refrigerant shortage rate that is the estimation result obtained by the first regression equation and a refrigerant shortage rate that is the estimation result obtained by the second regression equation, by a sigmoid curve using a sigmoid coefficient. Specifically, the cooling time refrigerant shortage rate calculation formula is (the sigmoid coefficient×the refrigerant shortage rate obtained by the first regression equation)+((1−the sigmoid coefficient)×refrigerant shortage rate obtained by the second regression equation). The estimation unit 45 calculates the refrigerant shortage rate of the refrigerant circuit 6 at the present time by substituting the current operating state quantity acquired by the acquisition unit 41 into the first regression equation and the second regression equation, and then substituting each of the calculated refrigerant shortage rates into the cooling time refrigerant shortage rate calculation formula.

Here, the sigmoid coefficient is calculated by using one of the operating state quantities. In the present embodiment, by taking into consideration that, if the subcool reaches 0, a result obtained by the first regression equation is approximately constant, a calculation formula is determined such that the sigmoid coefficient is 0.5 when the subcool is subcool is 5° C.

p=1/(1+exp(−(sc−5)))

- p: sigmoid coefficient
- sc: subcool value

As a result of determining the sigmoid coefficient in this way and using the sigmoid coefficient for the third cooling purpose estimation model 45A3, when the refrigerant shortage rate is in a range of 0% to 30%, that is, when the refrigerant shortage rate is in the first range, the estimated value of the first cooling purpose estimation model 45A1 is dominant in the estimated value obtained by the third cooling purpose estimation model 45A3, and, when the refrigerant shortage rate is in a range of 40% to 70%, that is, when the refrigerant shortage rate is in the second range, the estimated value of the second cooling purpose estimation model 45A2 is dominant in the estimated value obtained by the third cooling purpose estimation model 45A3.

In addition, the sigmoid coefficient need not always be calculated by the method as described above, but the sigmoid coefficient may be calculated such that, when the actual refrigerant shortage rate is equal to or larger than 30%, that is, when the actual refrigerant shortage rate is not in the first range, the estimated value of the second cooling purpose estimation model 45A2 is dominant in the estimated value obtained by the third cooling purpose estimation model 45A3, and when the actual refrigerant shortage rate is equal to or less than 40%, that is, when the actual refrigerant shortage rate is not in the second range, the estimated value of the first cooling purpose estimation model 45A1 is dominant in the estimated value obtained by the third cooling purpose estimation model 45A3.

The first heating purpose estimation model 45A4 is the estimation model 45A that is effective in the case where the refrigerant shortage rate is in a range of 0% to 20% (a third range), and is a fourth regression equation that is able to estimate the refrigerant shortage rate with high accuracy. The fourth regression equation is, for example, (α31×the degree of opening of the outdoor unit expansion valve 14)+α32. The estimation unit 45 calculates the refrigerant shortage rate by substituting, into the fourth regression equation, the current degree of opening of the outdoor unit expansion valve 14 that has been acquired by the acquisition unit 41. Moreover, the reason for substituting the degree of opening of the outdoor unit expansion valve 14 is to use the feature value that has been used to generate the first heating purpose estimation model 45A4.

The second heating purpose estimation model 45A5 is the estimation model 45A that is effective in the case where the refrigerant shortage rate is in a range of 30% to 70% (a fourth range), and is a fifth regression equation that is able to estimate the refrigerant shortage rate with high accuracy. The fifth regression equation is, for example, (α41×the degree of suction superheat)+(α42×the outdoor air temperature)+(α43×the rotation speed of the compressor 11)+(α44×the degree of opening of the outdoor unit expansion valve 14)+α45. It is assumed that the coefficients α41 to α45 are determined when the estimation models are generated. The estimation unit 45 calculates the refrigerant shortage rate of the refrigerant circuit 6 at the present time by substituting, into the fifth regression equation, the degree of suction superheat, the outdoor air temperature, the rotation speed of the compressor 11, and the degree of opening of the expansion valve at the main side that are acquired by the acquisition unit 41. Moreover, the reason for substituting the degree of suction superheat, the outdoor air temperature, the rotation speed of the compressor 11, and the degree of opening of the outdoor unit expansion valve 14 is to use the feature value that has been used to generate the second heating purpose estimation model 45A5. The degree of suction superheat is able to be calculated using an expression indicated by, for example, (the suction temperature—the low pressure saturation temperature). The outdoor air temperature is detected by the outdoor air temperature sensor 36. The rotation speed of the compressor 11 is detected by the rotation speed sensor (not illustrated) of the compressor 11. The degree of opening of the outdoor unit expansion valve 14 is detected by a sensor (not illustrated).

In addition, as described above, the refrigerant shortage rate that is able to be obtained by the fourth regression equation is 0% to 20%, and the refrigerant shortage rate that is able to be obtained by the fifth regression equation is 30% to 70%. In this case, in the case where the refrigerant shortage rate is in a range of 20% to 30%, if the fourth regression equation is used, the refrigerant shortage rate is calculated as 20%, whereas, if the fifth regression equation is used, the refrigerant shortage rate is calculated as 30%. In other words, in the case where the refrigerant shortage rate is in a range of 20% to 30%, both of the degree of opening of the outdoor unit expansion valve 14 exhibiting a high contribution rate when the refrigerant shortage rate is equal to or less than 20%, and the degree of suction superheat exhibiting a high contribution rate when the refrigerant shortage rate is equal to or larger than 30% are less likely to be changed, so that it is not possible to generate an effective estimation model. Therefore, if the fourth regression equation or the fifth regression equation is used, the refrigerant shortage rate largely differs depending on which model is used as illustrated in FIG. 8A.

The third heating purpose estimation model 45A6 is a heating time refrigerant shortage rate calculation formula that is able to cover the refrigerant shortage rate in a range of 0% to 70% that includes a range in which it is not able to estimate the refrigerant shortage rate by using any of the fourth regression equation and the fifth regression equation as described above. As illustrated in FIG. 8B, the heating time refrigerant shortage rate calculation formula continuously connects a refrigerant shortage rate that is the estimation result obtained by the fourth regression equation and a refrigerant shortage rate that is the estimation result obtained by the fifth regression equation by a sigmoid curve using a sigmoid coefficient. Specifically, the heating time refrigerant shortage rate calculation formula is (the sigmoid coefficient×the refrigerant shortage rate obtained by the fifth regression equation)+((1−the sigmoid coefficient)×the refrigerant shortage rate obtained by the fourth regression equation). The estimation unit 45 calculates the refrigerant shortage rate of the refrigerant circuit 6 at the present time by substituting the current operating state quantity acquired by the acquisition unit 41 into the fourth regression equation and the fifth regression equation, and then substituting each of the calculated refrigerant shortage rates into the heating time refrigerant shortage rate calculation formula.

Here, the sigmoid coefficient is calculated by using one of the operating state quantities, in the same manner as in the cooling operation. In the present embodiment, by taking into consideration that a result obtained by the fourth regression equation is approximately constant if the degree of opening of the outdoor unit expansion valve 14 is fully opened when the degree of opening of the outdoor unit expansion valve 14 is indicated by 0 in a case of a fully closed state and is indicated by 100 in a case of a fully opened state, a calculation formula is determined such that the sigmoid coefficient is 0.5 when the degree of opening of the outdoor unit expansion valve 14 is 90.

p=1/(1+exp(−(D/10−45)))

- p: sigmoid coefficient
- D: the degree of opening of the outdoor unit expansion valve 14

As a result of determining the sigmoid coefficient in this way and using the sigmoid coefficient for the third heating purpose estimation model 45A6, when the refrigerant shortage rate is in a range of 0% to 20%, that is, when the refrigerant shortage rate is in the third range, the estimated value of the first heating purpose estimation model 45A4 is dominant in the estimated value obtained by the third heating purpose estimation model 45A6, and, when the refrigerant shortage rate is in a range of 30% to 70%, that is, the refrigerant shortage rate is in the fourth range, estimated value of the second heating purpose estimation model 45A5 is dominant in the estimated value obtained by the third heating purpose estimation model 45A6.

In addition, the sigmoid coefficient need not always be calculated by the method as described above, but the sigmoid coefficient may be calculated such that, when the actual refrigerant shortage rate is equal to or larger than 20%, that is, when the actual refrigerant shortage rate is not in the third range, the estimated value of the second heating purpose estimation model 45A5 is dominant in the estimated value obtained by the third heating purpose estimation model 45A6, and when the actual refrigerant shortage rate is equal to or less than 30%, that is, when the actual refrigerant shortage rate is not the fourth range, the estimated value of the first heating purpose estimation model 45A4 is dominant in the estimated value obtained by the third heating purpose estimation model 45A6.

As described above, at the time of a cooling operation, the refrigerant shortage rate is estimated by using the first regression equation, the second regression equation, and the cooling time refrigerant shortage rate calculation formula. In the case where a value of the degree of supercooling of the refrigerant at the time of the cooling operation is larger than a first threshold (Tv1 in FIG. 7), it is possible to estimate the refrigerant shortage rate with good accuracy by selecting the first regression equation rather than selecting the second regression equation. In addition, in the case where the value of the degree of supercooling of the refrigerant at the time of the cooling operation is smaller than the first threshold, it is possible to estimate the refrigerant shortage rate with good accuracy by selecting the second regression equation rather than selecting the first regression equation. Furthermore, in the case where the value of the degree of supercooling of the refrigerant at the time of the cooling is near the first threshold, the estimated value of the refrigerant shortage rate largely varies depending on the regression equation to be used. Accordingly, at the time of the cooling operation, the cooling time refrigerant shortage rate calculation formula that includes the first regression equation and the second regression equation is selected. Consequently, it is possible to estimate the refrigerant shortage rate at the time of the cooling operation with good accuracy.

In addition, at the time of a heating operation, the refrigerant shortage rate is estimated by using the fourth regression equation, the fifth regression equation, and the heating time refrigerant shortage rate calculation formula. In the case where the degree of opening of the outdoor unit expansion valve 14 at the time of the heating operation is less than a second threshold (Tv2 in FIG. 8), it is possible to estimate the refrigerant shortage rate with good accuracy by selecting the fourth regression equation rather than selecting the fifth regression equation. In addition, in the case where the degree of opening of the outdoor unit expansion valve 14 at the time of the heating operation is not less than the second threshold, it is possible to estimate the refrigerant shortage rate with good accuracy by selecting the fifth regression equation rather than selecting the fourth regression equation. Furthermore, in the case where the value of the degree of opening of the outdoor unit expansion valve 14 at the time of the heating operation is near the first threshold, the estimated value of the refrigerant shortage rate largely varies depending on the regression equation to be used. Accordingly, at the time of the heating operation, the heating time refrigerant shortage rate calculation formula that includes the fourth regression equation and the fifth regression equation is selected. Consequently, it is possible to estimate the refrigerant shortage rate at the time of the heating operation with good accuracy.

The determination model 46A is generated by using a simulation value that is a value of the second feature value that is obtained as a result of a simulation of an operation of the refrigerant circuit 6 when the refrigerant circuit 6 is operated normally and when only the remaining refrigerant amount is changed. The determination unit 46 calculates an outlier by applying detection value of the second feature value that has been acquired from the air conditioner 1 that is in operation to the determination model 46A. The determination unit 46 determines, on the basis of the value of the outlier, whether or not the detection value of the first feature value that has been acquired by the detection unit at the same time as the detection value of the second feature value is a detection value that is to be used to estimate the refrigerant shortage rate performed by the estimation unit 45.

To generate the determination model 46A, for example, a Kernel density estimation method is used. The Kernel density estimation method is a method for estimating a density function of the entire distribution from limited sample points. The determination model 46A calculates a degree of deviation (hereinafter, also referred to as an “outlier”) from a local maximum value (a center of a cluster (a set of data having similarities)) of the density function on the basis of the density function of the entire distribution that has been estimated from the limited sample points. Then, if data targeted for determination is input, the determination model 46A calculates an outlier of the input data, and determines whether or not the outlier is within a predetermined range (whether or not the data targeted for determination is included in the cluster).

FIG. 9 is a diagram illustrating one example of a distribution of the detection values of the second feature values. As illustrated in FIG. 9, the determination model 46A recognizes, as a single cluster, a set of the values of the second feature values (hereinafter, also referred to as “simulation values of the second feature values”) that are obtained by a simulation, and classifies the cluster as a normal state. A condition for the simulation is that the refrigerant circuit 6 is in a steady state (in a state in which a fill volume of a refrigerant is a defined amount), or the state in which a fill volume of a refrigerant is decreased (refrigerant leakage state). The simulation values of the second feature value in the steady state are values of the second feature values that are obtained by a simulation that is performed by assuming a state in which each of the components (the refrigerant circuit 6, the compressor, the expansion valve, etc.) constituting the air conditioner 1 are operated normally. In addition, the simulation values of the second feature values in the refrigerant leakage state are values of the second feature values that are obtained by a simulation that is performed by assuming a state in which each of the components (the refrigerant circuit 6, the compressor, the expansion valve, etc.) constituting the air conditioner are operated normally and by assuming a state in which only an amount of refrigerant remaining in the refrigerant circuit 6 is changed (decreased). Furthermore, if a detection value of the second feature value that deviates from the cluster that is classified as normal by the determination model 46A is input, the detection value is classified as abnormal. Moreover, the detection value that is classified as abnormal is a detection value that deviates from the cluster that is classified as normal when the detection value is plotted on a graph as illustrated in FIG. 9. In addition, abnormal mentioned here indicates a state in which a failure is highly likely to have occurred in a device constituting the refrigerant circuit.

The determination model 46A calculates an outlier by applying the detection value of the second feature value that has been acquired from the air conditioner 1 that is in operation. Specifically, the determination model 46A adopts the values of the second feature values that are used to generate the determination model 46A as normal sample values (the cluster that has been classified as normal), and calculates an outlier that indicates the degree of deviation related to the detection values of the second feature values that have been acquired by the acquisition unit 41 included in the air conditioner 1 that is in operation. The outlier is obtained by quantifying a distance that indicates a degree of deviation from the center of the cluster that is classified as normal, and the degree of deviation is increased as the absolute value of the quantified value is larger. As a result, as the degree of deviation is increased, the possibility that the detection value of the second feature value is abnormal is increased.

FIG. 10 is a diagram illustrating an example of anomaly detection obtained by the outlier. The determination unit 46 classifies the detection value of the second feature value as normal in the case where the outlier of the detection value of the second feature value is larger than, for example, “−150” (in the case where the absolute value of the outlier is less than 150), and classifies the detection value of the second feature value as abnormal in the case where the outlier of the detection value of the second feature value is equal to or less than, for example, “−150” (in the case where the absolute value of the outlier is equal to or larger than 150). Moreover, an outlier threshold X can be set to a certain value by which normal data is not erroneously detected as abnormal based on, for example, a result of verification of values that are actually determined as abnormal and that are obtained by collecting failure histories of the air conditioner 1. Therefore, in the case where the detection value of the second feature value is classified as abnormal, the determination unit 46 does not cause the estimation unit 45 to perform estimation operation of the refrigerant shortage rate by using the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value. Furthermore, the determination unit 46 stores the detection value of the second feature value that has been classified as abnormal in the anomaly log storage unit 43A as an anomaly log.

If the absolute value of the calculated outlier is less than the outlier threshold X, for example, 150, the determination unit 46 classifies the detection value of the second feature value as normal. In this case, the determination unit 46 causes the estimation unit 45 to perform an estimation operation of the refrigerant shortage rate by using the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value.

In addition, for convenience of description, the outlier threshold X is set to, for example, “−150”, but the threshold may appropriately be adjusted on the basis of the result of collecting failure histories and verifying the values that are actually determined as abnormal.

FIG. 11 is a flowchart illustrating one example of a processing operation related to an estimation process performed by the control circuit 19. Moreover, it is assumed that the estimation unit 45 included in the control circuit 19 stores therein the first cooling purpose estimation model 45A1, the second cooling purpose estimation model 45A2, the third cooling purpose estimation model 45A3, the first heating purpose estimation model 45A4, the second heating purpose estimation model 45A5, the third heating purpose estimation model 45A6 that are generated in advance. Furthermore, it is assumed that the determination unit 46 included in the control circuit 19 stores therein the cooling time determination model 46B and the heating time determination model 46C that are generated in advance. The estimation process is periodically performed, for example, in a predetermined period of time (for example, night time) once a day with respect to operating state quantities that are sequentially detected every 10 minutes in 24 hours by the detection unit. Moreover, the night time is described as an example of the predetermined period of time; however, for example, the operating state quantities corresponding to one day are acquired after the operation of the air conditioner 1 is stopped in the night time that is a period of time in which operation frequency of the air conditioner 1 is low. In addition, as the predetermined period of time, it is possible to determine a predetermined time in which the operation is not performed instead of the night time by checking the working states of the air conditioner 1 for, for example, one month.

In FIG. 11, the control unit 44 included in the control circuit 19 collects the operating state quantities as pieces of operation data by way of the acquisition unit 41 (Step S11). The control unit 44 performs a data filtering process for extracting an arbitrary operating state quantity from among the collected pieces of operation data (Step S12). The control unit 44 performs a data cleansing process (Step S13). Furthermore, the determination unit 46 uses the determination model 46A and performs a determination process for classifying whether the detection value of the second feature value obtained after the data cleansing process is normal or abnormal (Step S14).

The control unit 44 determines whether the detection value of the second feature value has been classified as normal or abnormal (Step S15). If the detection value of the second feature value has been classified as normal at Step S15, the estimation unit 45 performs a remaining refrigerant amount estimation process for applying, to each of the estimation models, the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that has been classified as normal (Step S16). Then, the estimation unit 45 calculates the refrigerant shortage rate of the refrigerant circuit 6 (Step S17), and ends the processing operation illustrated in FIG. 11.

In addition, if the detection value of the second feature value has been classified as abnormal at Step S15, the determination unit 46 performs an abnormality output process for storing the detection value of the second feature value that has been classified as abnormal in the anomaly log storage unit 43A and outputting an alert (Step S18), and then, ends the processing operation illustrated in FIG. 11.

The data filtering process is a process for, instead of using all of the plurality of operating state quantities, extracting, on the basis of a predetermined filter condition, only a part of the operating state quantities (the detection value of the first feature value and the detection value of the second feature value) that are needed to perform the determination process on the second feature value or that is needed to calculate the refrigerant shortage rate from among the plurality of operating state quantities. By substituting, into the generated estimation model 45A and the determination model 46A, the detection value of the first feature value and the detection value of the second feature value that haven been subjected to the data filtering process, which will be described later, (obtained by subtracting an abnormal value and an outstanding value), it is possible to more accurately perform the determination process by using the second feature value and perform estimating the refrigerant shortage rate performed by using the first feature value.

The predetermined filter condition includes a first filter condition, a second filter condition, and a third filter condition. The first filter condition is a filter condition for data that is extracted commonly among, for example, all of the operation modes of the air conditioner 1. The second filter condition is a filter condition for data that is extracted at the time of the cooling operation. The third filter condition is a filter condition for data that is extracted at the time of the heating operation.

The first filter condition is, for example, a drive state of the compressor 11, identification of an operation mode, elimination of a special operation, elimination of a missing value included in an acquired value, selection of a small value of an amount of change in an operating state quantity that largely affects generation of each of the regression equations, or the like. The drive state of the compressor 11 is a condition that is needed to be determined because of an inability to estimate the refrigerant shortage rate unless the refrigerant circulates in the refrigerant circuit 6 as a result of stable operation of the compressor, and is a filter condition that is provided to eliminate an operating state quantity that has been detected in a transition period, such as at the time of a start-up of the compressor 11.

The identification of the operation mode is a filter condition for extracting only an operating state quantity that has been acquired at the time of the cooling operation and at the time of the heating operation. Therefore, the operating state quantity that has been acquired at the time of a dehumidification operation or an air supply operation is eliminated. The elimination of the special operation is a filter condition for excluding the operating state quantity that is acquired at the time of the special operation, such as an oil recovery operation or a defrosting operation, in which the state of the refrigerant circuit 6 is largely different from the state at the time of a cooling operation and at the time of a heating operation. The elimination of the missing value is a filter condition for excluding the operating state quantity that includes a missing value because accuracy may possibly be decreased if, in the case where a missing value is included in the operating state quantity that is used to determine the refrigerant shortage rate, each or the regression equation is generated by using the operating state quantity.

The selection of the small value of the amount of change in the operating state quantity that is substituted in each of the regression equations or each of the refrigerant shortage rate calculation formulas is a filter condition for extracting only an operating state quantity in the case where the operation state of the air conditioner 1 is stable, and is a condition that is needed to improve the accuracy of estimation performed by using each of the regression equations and each of the refrigerant shortage rate calculation formulas. Moreover, the operating state quantity that has large influence is, for example, the degree of supercooling of the refrigerant that is used when the refrigerant shortage rate at the time of a cooling operation is in a range of 0 to 30%, the suction temperature that is used when the refrigerant shortage rate at the time of a cooling operation is in a range of 40 to 70%, the degree of suction superheat at the time of a heating operation, or the like.

The second filter condition includes, for example, elimination of the heat exchange outlet temperature, abnormality of the subcool, abnormality of the discharge temperature, or the like.

The elimination of the heat exchange outlet temperature is a filter condition that takes into consideration the fact that, as a result of the outdoor air temperature sensor 36 and a heat exchange outlet temperature sensor 35 are arranged at positions that are close to each other, the heat exchange outlet temperature that has been detected by the heat exchange outlet temperature sensor 35 at the time of a cooling operation does not become lower than the outdoor air temperature that has been detected by the outdoor air temperature sensor 36, and is a filter condition for excluding the heat exchange outlet temperature that is lower than the outdoor air temperature.

The abnormality of the subcool is a filter condition for excluding, when the degree of supercooling of the refrigerant that is abnormally high or abnormally low has been detected, this state that has occurred caused by an extremely large or small cooling load. The abnormality of the discharge temperature is a filter condition for excluding the discharge temperature that has been detected when what is called an out-of-gas state is detected in which the amount of refrigerant that is sucked into the compressor 11 is decreased due to a small cooling load.

The third filter condition is, for example, abnormality of a discharge temperature or the like. The third filter condition is a filter condition for excluding a discharge temperature that has been detected when, the discharge temperature is decreased by reducing the rotation speed of, for example, the compressor 11 in the case where the discharge temperature is increased caused by a large heating load at the time of a heating operation and discharge temperature protection control is performed.

The data cleansing process is a process for excluding the detection value of the first feature value that may lead to erroneous estimation, instead of using all of the acquired detection values of the first feature values for estimation of the refrigerant shortage rate. In addition, the data cleansing process is also a process for excluding the detection value of the second feature value that may lead to erroneous determination, instead of using all of the acquired detection values of the second feature values for the determination process. Specifically, the data cleansing process includes noise suppression that is performed by smoothing the acquired operating state quantities, data amount limitation, or the like. The noise suppression performed by smoothing data is a process for preventing noise by calculating a mean value in a subject interval and calculating a moving average of, for example, the degree of supercooling of the refrigerant, the suction temperature, and the degree of suction superheat in each of the models. The data amount limitation is a process for eliminating data, for example, whose amount is small because reliability of this kind of data is low. For example, if the number of pieces of data remaining after the filtering process performed on the pieces of input data with an amount corresponding to one day is equal to or larger than X, the data is used for the estimation process on the refrigerant shortage rate or is used for the determination process on the second feature value, and, if the number of pieces of data is smaller than X, all of the pieces of the data obtained on that day are not used. In other words, in the data cleansing process, it is possible to more accurately estimate the refrigerant shortage rate by substituting, into the estimation model 45A, the operating state quantities from which the abnormal value and the outstanding value are excluded, and it is possible to more accurately determine the second feature value by substituting, into the determination model 46A, the operating state quantities from which the abnormal value and the outstanding value are excluded.

The determination process is a process for calculating the degree of deviation (outlier) from a local maximum value (the center of a cluster) of the density function on the basis of the density function of the entire distribution that has been estimated from the simulation values of the second feature values, and determining whether the outlier is within a predetermined range (whether data targeted for determination is included in the cluster). The outlier is calculated by applying, to the determination model 46A, the detection values of the second feature values that have been acquired from the air conditioner 1 that is in operation. In the determination process, the outlier from the detection value of the second feature value is calculated by using the value of the second feature value, which has been used to generate the determination model 46A, as the normal sample value. Furthermore, in the determination process, if the absolute value of the calculated outlier is equal to or larger than the absolute value of the outlier threshold X, the detection value of the second feature value is classified as abnormal. Furthermore, in the determination process, if the absolute value of the calculated outlier is less than the absolute value of the outlier threshold X, the detection value of the second feature value is classified as normal.

FIG. 12 is a flowchart illustrating one example of the processing operation related to the remaining refrigerant amount estimation process performed by the control circuit 19. The estimation of the remaining refrigerant amount is a process for calculating the refrigerant shortage rate of the refrigerant circuit 6 at the present time by substituting, into each of the regression equations or each of the refrigerant shortage rate calculation formulas of the estimation model 45A, the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that is classified as normal in the determination process from among the current operating state quantities (sensor values) that are subjected to the data filtering process and the data cleansing process. In FIG. 12, the estimation unit 45 included in the control circuit 19 determines whether or not the acquired first feature value is acquired during a cooling operation (Step S21). If the acquired first feature value is acquired during a cooling operation (Yes at Step S21), the estimation unit 45 applies the first feature value to each of the first cooling purpose estimation model 45A1 to the third cooling purpose estimation model 45A3 (Step S22).

If the acquired first feature value is not acquired during a cooling operation (No at Step S21), that is, if the acquired first feature value is acquired during a heating operation, the estimation unit 45 applies the first feature value to each of the first heating purpose estimation model 45A4 to the third heating purpose estimation model 45A6 (Step S23). Then, the estimation unit 45 calculates the refrigerant shortage rate at the present time by combining the result obtained by applying the first feature value to each of the first cooling purpose estimation model 45A1 to the third cooling purpose estimation model 45A3 and the result obtained by applying the first feature value to each of the first heating purpose estimation model 45A4 to the third heating purpose estimation model 45A6 (Step S24), and ends the processing operation illustrated in FIG. 12.

In the abnormality output process, the detection value of the second feature that is classified as abnormal in the determination process is stored as an anomaly log in the anomaly log storage unit 43A, and an alarm is output. As a result, it is possible to recognize abnormality of the detection value of the second feature value.

The estimation unit 45 calculates the current refrigerant shortage rate of the refrigerant circuit 6, and notifies the control unit 44 of the calculated refrigerant shortage rate. Furthermore, the determination unit 46 classifies the current second feature value as normal or abnormal, and notifies the control unit 44 of the classification result. The control unit 44 determines, on the basis of the refrigerant shortage rate calculated by the estimation unit 45, whether the amount of refrigerant is abnormal or normal, and outputs the determination result as a refrigerant amount determination result. The control unit 44 outputs the state of the air conditioner 1 on the basis of the refrigerant amount determination result and the classification result that is obtained by the determination unit 46. FIG. 13 is a diagram illustrating one example of a failure decision table 44A included in the control unit 44.

The control unit 44 refers to the failure decision table 44A and determines, in the case where the refrigerant amount determination result is abnormal and the classification result obtained by the determination unit 46 is abnormal, that detection of a refrigerant leakage is caused by another failure, and then, outputs an alarm that indicates the determination content. The control unit 44 refers to the failure decision table 44A and determines, in the case where the refrigerant amount determination result is abnormal and the classification result obtained by the determination unit 46 is normal, that a refrigerant leakage has been detected, and then, outputs an alarm that indicates the determination content. In addition, the control unit 44 refers to the failure decision table 44A and determines, in the case where the refrigerant amount determination result is normal and the classification result obtained by the determination unit 46 is abnormal, that a failure other than a refrigerant leakage has been detected, and then, outputs an alarm that indicates the determination result. In addition, the control unit 44 refers to the failure decision table 44A, and determines, if the refrigerant amount determination result is normal and the classification result obtained by the determination unit 46 is normal, that this state is the steady state.

Effects of First Embodiment

In the air conditioner 1 according to the first embodiment, the value of the second feature value that is used to generate the determination model 46A is defined as a normal sample value, and an outlier of the detection value of the second feature value is calculated.

Furthermore, in the air conditioner 1, if the absolute value of the calculated outlier is equal to or larger than the absolute value of the outlier threshold X, the detection value of the second feature value is classified as abnormal. Furthermore, in the air conditioner 1, the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that is classified as abnormal is not used for the estimation model 45A. As a result, it is possible to prevent an erroneous refrigerant shortage rate from being estimated.

For example, in the case where, when the refrigerant shortage rate is estimated by the estimation model 45A that has been generated by the linear analysis obtained from the multiple regression analysis, the first feature value is changed as a result of a failure that is other than a refrigerant leakage and that has occurred together with a refrigerant leakage, it may also be conceivable that the refrigerant shortage rate is estimated as a small value even though the refrigerant shortage rate is originally increased (=abnormal) depending on the degree of a change in each of the feature values. For example, it may also be conceivable that the refrigerant shortage rate is estimated as a small value (=normal) as a result of a change in the rotation speed of the compressor and the suction temperature caused by a failure other than a refrigerant leakage, and as a result of the amount of change in each of the values being canceled out. However, in the air conditioner 1 according to the present embodiment, the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that has been classified as abnormal by the determination model 46A that is generated by the non-linear analysis, such as the Kernel density estimation method, is not used. As a result, it is possible to prevent an erroneous refrigerant shortage rate from being estimated.

In addition, originally, if the estimation model 45A that is generated by the linear analysis is used, it may also be conceivable that the refrigerant shortage rate is estimated to increase (=abnormal) even though the refrigerant shortage rate is a small value (=normal). For example, it may also be conceivable that the refrigerant shortage rate is erroneously estimated to increase as a result of a change in the rotation speed of the compressor caused by a failure other than the refrigerant leakage. However, in the air conditioner 1 according to the first embodiment, the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that is classified as abnormal by the determination model 46A that is generated by the non-linear analysis is not used for the estimation model 45A. As a result, it is possible to prevent an erroneous refrigerant shortage rate from being estimated.

If the absolute value of the calculated outlier is less than the absolute value of the outlier threshold, the determination model 46A included in the air conditioner 1 classifies the detection value of the second feature value as normal. Then, the air conditioner 1 calculates the refrigerant shortage rate of the refrigerant circuit 6 by performing the multiple regression analysis on the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that is classified as normal. As a result, it is possible to accurately estimate the refrigerant shortage rate of the refrigerant circuit 6.

The determination model 46A that is mounted on the air conditioner 1 is generated by the non-linear analysis, such as the Kernel density estimation method, by using a part of the detection value of the first feature value that is used for the estimation model 45A and by using the value of the second feature value that includes operating state quantity that largely affects a cooling cycle operation. The determination model 46A classifies the detection value of the second feature value as normal or abnormal. Then, in the estimation model 45A, the estimation model 45A is generated by using the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that is classified as normal, instead of using all of the operating state quantities. As a result, it is possible to generate the estimation model 45A with high accuracy.

In the present embodiment, each of the regression equations of the estimation model 45A is generated by using the feature value that is obtained by a simulation, and, in the feature value that is obtained by the simulation, an abnormal value and a value that is extremely larger or smaller than other values are not included. The detection value of the operating state quantity obtained by performing the data filtering process and the data cleansing process and excluding an abnormal value and an outstanding value is substituted into each of the regression equations or each of the refrigerant shortage rate calculation formulas of the estimation model 45A that is generated by using the feature value obtained by the simulation as described above. At this time, by substituting only the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that is classified as normal by using the determination model 46A, it is possible to more accurately estimate the refrigerant shortage rate.

The determination model 46A is generated by using the feature value that is obtained by the simulation, and, in the feature value that is obtained by the simulation, an abnormal value and a value that is extremely larger or smaller than other values are not included. By applying the detection value of the second feature value obtained by performing the data filtering process and the data cleansing process and excluding an abnormal value and an outstanding value as described above to the determination model 46A that is generated by using the feature value that does not include the abnormal value and the outstanding value, it is possible to more accurately determine the detection value of the second feature value. Furthermore, by performing the data filtering process and the data cleansing process, the control circuit 19 is able to decrease an amount of data that is used to calculate the outlier by the determination model 46A, so that it is possible to reduce the time needed for the calculation of the outlier by using the determination model 46A and it is possible to reduce a load applied on the control circuit 19.

In addition, in the present embodiment, as the second feature value, the operating state quantities illustrated in FIG. 5 and FIG. 6 are used; however, if the determination model 46A is generated by detecting a larger volume of operating state quantity by mounting a larger number of sensors on the refrigerant circuit, and if the determination model 46A generated in this way is used, it is possible to increase a possibility of detection of various failures. In addition, regarding the first feature value, by narrowing down to the state quantity that has the correlation with a decrease in an amount of refrigerant, it is possible to a remaining refrigerant amount with good accuracy.

Moreover, in the first embodiment described above, a case has been described as an example in which the simulation result of each of the operating state quantities is obtained at the design stage of the air conditioner 1, and the control circuit 19 stores the estimation model 45A and the determination model 46A that are obtained by causing an information processing apparatus, such as a server, having a learning function to learn a simulation result. Instead of this, it may be possible to provide a server 120 that is connected to the air conditioner 1 by a communication network 110, and cause the server 120 to generate the estimation model 45A and the determination model 46A and transmit an estimation result of the estimation model 45A to the air conditioner 1, and an embodiment thereof will be described below.

Second Embodiment
<Configuration of Air Conditioning System>

FIG. 14 is a diagram illustrating one example of an air conditioning system 100 according to a second embodiment. In addition, by assigning the same reference numerals to components having the same configuration as those in the air conditioner 1 according to the first embodiment, overlapped descriptions of the configuration and the operation thereof will be omitted. The air conditioning system 100 illustrated in FIG. 14 includes the air conditioner 1, the communication network 110, and the server 120. The air conditioner 1 includes the compressor 11, the outdoor unit 2 that includes the outdoor heat exchanger 13 and the outdoor unit expansion valve 14, the indoor unit 3 that includes the indoor heat exchanger 51, and a control circuit 19A. The air conditioner 1 includes the refrigerant circuit 6 that is constituted by connecting both of the outdoor unit 2 and the indoor unit 3 by refrigerant pipes, such as the liquid pipe 4 and the gas pipe 5, and a predetermined amount of refrigerant is filled in the refrigerant circuit 6. The control circuit 19A includes the acquisition unit 41, the communication unit 42 that is the first communication unit, the storage unit 43, and the control unit 44. In addition, it is assumed that the control circuit 19A does not include the estimation unit 45, the determination unit 46, and the anomaly log storage unit 43A.

The server 120 includes a generation unit 121, a communication unit 121A that is a second communication unit, an estimation unit 122, a determination unit 123, and a storage unit 124. The storage unit 124 includes an anomaly log storage unit 124A. The generation unit 121 generates the estimation model 45A by a multiple regression analysis method by using a detection value or a simulation value of the first feature value related to estimation of the refrigerant shortage rate of the refrigerant that is filled in the refrigerant circuit 6. Moreover, the estimation model 45A includes, for example, the first cooling purpose estimation model 45A1, the second cooling purpose estimation model 45A2, the third cooling purpose estimation model 45A3, the first heating purpose estimation model 45A4, the second heating purpose estimation model 45A5, and the third heating purpose estimation model 45A6 that are described in the first embodiment. The estimation unit 122 stores therein the estimation model 45A that has been generated by the generation unit 121. Furthermore, the generation unit 121 generates the determination model 46A by the Kernel density estimation method by using the second feature value. In addition, the determination model 46A includes, for example, the cooling time determination model 46B and the heating time determination model 46C that are described in the first embodiment.

The determination unit 123 stores therein the determination model 46A that has been generated by the generation unit 121. The determination unit 123 classifies the detection value of the second feature value as normal or abnormal by using the determination model 46A. In the case where the detection value of the second feature value is classified as abnormal, the determination unit 123 stores the detection value of the second feature value that has been classified as abnormal in the anomaly log storage unit 124A as an anomaly log.

Furthermore, the estimation unit 122 calculates the refrigerant shortage rate in the refrigerant circuit 6 included in the air conditioner 1 by using the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that is classified as normal by the determination model 46A and by using the received estimation model 45A. The communication unit 121A transmits the refrigerant shortage rate that has been calculated by the estimation unit 122 to the air conditioner 1 via the communication network 110.

The generation unit 121 generates or updates the cooling time determination model 46B by using the values of the second feature values obtained by a simulation performed in the steady state and the refrigerant leakage state at the time of a cooling operation performed when the refrigerant circuit 6 is in a normal state.

The generation unit 121 periodically collects the operating state quantities at the time of a cooling operation from a standard machine (installed in a test room or the like of a manufacturing company) of the air conditioner 1 that is able to measure the steady state and the refrigerant leakage state at the time of a cooling operation when the refrigerant circuit 6 is in a normal state, and generates or updates the cooling time determination model 46B by using a comparison result, which has been obtained by comparing a classification result that indicates normal or abnormal and that is obtained by the cooling time determination model 46B to an actually measured classification result, and by using the collected operating state quantities. As a result, it is possible to generate the cooling time determination model 46B with high accuracy.

The generation unit 121 periodically collects the operating state quantities at the time of a cooling operation from the standard machine (installed in the test room or the like of the manufacturing company) of the air conditioner 1 that is able to measure the refrigerant shortage rate of the refrigerant circuit 6, and generates or updates the first cooling purpose estimation model 45A1, the second cooling purpose estimation model 45A2, and the third cooling purpose estimation model 45A3 by using a comparison result, which has been obtained by comparing the refrigerant shortage rate that is estimated by each of the estimation models that are included in the estimation model 45A to an actually measured refrigerant shortage rate, and by using the collected operating state quantities. In addition, as in the first embodiment, it may be possible to obtain, by a simulation, the operating state quantity that is used to generate each of the estimation models, and then, the generation unit 121 may generate each of the estimation models that are included in the estimation model 45A by using each of the operating state quantities that are obtained by the simulation.

The generation unit 121 generates or updates the heating time determination model 46C by using the values of the second feature values that are obtained by a simulation in the steady state and the refrigerant leakage state at the time of a heating operation when the refrigerant circuit 6 is in the normal.

The generation unit 121 periodically collects the operating state quantities at the time of a heating operation from the standard machine (installed in the test room or the like of the manufacturing company) of the air conditioner 1 that is able to measure the steady state and the refrigerant leakage state at the time of a heating operation when the refrigerant circuit 6 is in the normal state, and generates or updates the heating time determination model 46C by using a comparison result, which has been obtained by comparing the classification result that indicates normal or abnormal and that is obtained by the heating time determination model 46C to the actually measured classification result, and by using the collected operating state quantities. As a result, it is possible to generate the heating time determination model 46C with high accuracy.

The generation unit 121 periodically collects the operating state quantities at the time of a heating operation from the standard machine of the air conditioner 1 described above, and generates the first heating purpose estimation model 45A4, the second heating purpose estimation model 45A5, and the third heating purpose estimation model 45A6 by using a comparison result, which has been obtained by comparing the refrigerant shortage rate that is estimated by each of the estimation models that are included in the estimation model 45A to the actually measured refrigerant shortage rate, and by using the collected operating state quantities. In addition, as in the first embodiment, it may be possible to obtain, by a simulation, the operating state quantities that is used to generate each of the estimation models 45A, and then, the generation unit 121 may generate each of the estimation models that are included in the estimation model 45A by using each of the operating state quantities that are obtained by the simulation.

The generation unit 121 generates the determination model 46A by using the feature value that has been obtained by the simulation, and the value of the feature value that has been obtained by the simulation does not include an abnormal value and an extremely large or small value as compared to other values. By applying the detection value of the second feature value from which the abnormal value and the outstanding value are excluded by performing the data filtering process and the data cleansing process as described above to the determination model 46A that is generated by using the value of the feature value that does not include the abnormal value and the outstanding value, it is possible to implement further accurate determination of the detection value of the second feature value. Furthermore, if, in the generation unit 121, the data filtering process and the data cleansing process is performed on the second feature value as described in the first embodiment, it is possible to decrease an amount of data that is used to calculate the outlier by the determination model 46A. As a result, it is possible to reduce the time needed to calculate the outlier by the determination model 46A and it is thus possible to reduce a usage rate of the server 120, so that it is possible to reduce the cost needed to calculate the outlier in a case of a measured rate system in which cost is increased in accordance with an amount used by the server 120.

Effects of Second Embodiment

The server 120 according to the second embodiment generates the determination model 46A by using the values of the second feature values that are obtained by the simulation in the steady state and the refrigerant leakage state when the refrigerant circuit 6 is in the normal state, and stores the generated determination model 46A in the determination unit 123. The determination unit 123 included in the server 120 is able to classify, by using the stored determination model 46A, whether each of the detection values of the second feature values that are acquired at a different timing is normal or abnormal.

The server 120 generates the estimation model 45A by using the value of the first feature value that has been acquired from the air conditioner 1, and stores the generated estimation model 45A in the estimation unit 122. The server 120 estimates the refrigerant shortage rate by using the stored estimation model 45A, and transmits the estimation result to the air conditioner 1 via the communication network 110. As a result, the air conditioner 1 is able to recognize the refrigerant shortage rate of the refrigerant circuit 6.

In addition, a case has been described as an example in which, in the air conditioner 1 according to the first and the second embodiments, the estimation model 45A and the determination model 46A that are used to estimate the refrigerant shortage rate occurring in the case where the N indoor units 3 are connected to the single outdoor unit 2. In contrast, the air conditioner 1 constituted such that the single outdoor unit 2 and the single indoor unit 3 are connected is also able to estimate the refrigerant shortage rate by using the same method as that described in the first embodiment or the second embodiment. The air conditioner 1 having this configuration will be described as a third embodiment below.

Third Embodiment

In the case where a ratio of the number of one outdoor units to the number of indoor units is one to one, the control circuit includes a fourth cooling purpose estimation model that estimates a refrigerant shortage rate at the time of a cooling operation at the present time, and a fifth heating purpose estimation model that estimates a refrigerant shortage rate at the time of a heating operation at the present time. In addition, for convenience of description, by assigning the same reference numerals to components having the same configuration as those in the air conditioner 1 according to the first embodiment, overlapped descriptions of the configuration and the operation thereof will be omitted. The air conditioner 1 according to the third embodiment is different from the air conditioner 1 according to the first embodiment in that the number of the indoor units 3 to be provided is one, the fourth cooling purpose estimation model that has been generated by using an operating state quantity that is different from the operating state quantities that are used in the first to the third cooling purpose estimation models 45A1, 45A2, and 45A3 is used, and the fourth heating purpose estimation model that has been generated by using an operating state quantity that is different from the operating state quantities that are used in the first to the third heating purpose estimation models 45A4, 45A5, and 45A6 is used.

The fourth cooling purpose estimation model is a seventh regression equation that has been generated by using the multiple regression analysis method. The seventh regression equation is, for example, (α71×the outdoor heat exchange temperature)−(α72× the outdoor air temperature)−(+73× the discharge temperature)+(α74×the rotation speed of the compressor 11)−(α75×the degree of opening of the expansion valve)+α76. It is assumed that the coefficients α71 to α75 are determined at the time of generation of the estimation model. The estimation unit 45 calculates the refrigerant shortage rate at the present time by substituting, into the seventh regression equation,

- the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that has been classified as normal in the determination model 46A from among the current operating state quantities that are subjected to data cleansing, that is, for example, the outdoor heat exchange temperature, the outdoor air temperature, the discharge temperature, the rotation speed of the compressor 11, and the degree of opening of the expansion valve. Moreover, the reason for substituting the outdoor heat exchange temperature, the outdoor air temperature, the discharge temperature, the rotation speed of the compressor 11, and the degree of opening of the expansion valve is to use the feature value that is used to generate the fourth cooling purpose estimation model. In addition, the outdoor heat exchange temperature is detected by the refrigerant temperature sensor 35.

The fourth heating purpose estimation model is an eighth regression equation that has been generated by using the multiple regression analysis method. The eighth regression equation is, for example, (α81×the indoor heat exchange temperature)+(α82×the rotation speed of the compressor 11)+(α83×the outdoor air temperature)−(α84×the outdoor heat exchange temperature)−(α85×the degree of opening of the expansion valve)+α86. It is assumed that the coefficients α81 to α85 are determined at the time of generation of the estimation model. The estimation unit 45 calculates the refrigerant shortage rate at the present time by substituting, into the eighth regression equation, the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value that has been classified as normal in the determination model 46A from among the current operating state quantities that are subjected to data cleansing, that is, for example, the indoor heat exchange temperature, the rotation speed of the compressor 11, the outdoor air temperature, the outdoor heat exchange temperature, the outdoor air temperature, the discharge temperature, and the degree of opening of the expansion valve. Moreover, the reason for substituting the indoor heat exchange temperature, the rotation speed of the compressor 11, the outdoor air temperature, the outdoor heat exchange temperature, the outdoor air temperature, the discharge temperature, and the degree of opening of the expansion valve is to use the feature value that is used to generate the fourth heating purpose estimation model. In addition, the indoor heat exchange temperature at the time of a heating operation is able to be converted from the pressure value that has been detected by the discharge pressure sensor 31.

In addition, in the present embodiment, a case has been described as an example in which a relative amount of refrigerant that represents an amount of refrigerant remaining in the refrigerant circuit 6 is estimated. Specifically, a case has been described as an example in which the refrigerant shortage rate, which is the ratio of the amount of refrigerant that has leaked to the outside from the refrigerant circuit 6 to a fill volume (initial value) at the time at which a refrigerant is filled in the refrigerant circuit 6, is estimated and provided. However, the present invention is not limited to this, and it may be possible to provide an amount of refrigerant that has leaked to the outside from the refrigerant circuit 6 by multiplying the initial value by the estimated refrigerant shortage rate. In addition, it may be possible to generate an estimation model for estimating an absolute amount of the refrigerant that has leaked to the outside from the refrigerant circuit 6 or an absolute amount of the refrigerant that remains in the refrigerant circuit 6, and provide an estimation result that is obtained by the estimation model. In the case where an estimation model for estimating an absolute amount of the refrigerant that has leaked to the outside from the refrigerant circuit 6 or an absolute amount of the refrigerant that remains in the refrigerant circuit 6 is generated, in addition to each of the operating state quantities as described above, volumes of the outdoor heat exchanger 13 and each of the indoor heat exchangers 51 and a volume of the liquid pipe 4 may be considered.

Modification

Furthermore, in the present embodiment, for example, a case has been described as an example in which the estimation result of the first cooling purpose estimation model 45A1 and the estimation result of the second cooling purpose estimation model 45A2 are interpolated by the sigmoid coefficients; however, the example is not limited to the sigmoid coefficients, and, for example, an interpolate method, such as linear interpolate, may be used, and appropriate modifications are possible.

In the present embodiment, some of simulation results are used from among the plurality of simulation results, instead of using all of the simulation results. For example, the first cooling purpose estimation model 45A1 that is used when the refrigerant shortage rate at the time of a cooling operation is in a range of 0 to 30%, the second cooling purpose estimation model 45A2 that is used when the refrigerant shortage rate is in a range of 40 to 70%, the third cooling purpose estimation model 45A3 that is used when the refrigerant shortage rate is in a range of 30 to 40% are generated in a separate manner. Therefore, the operating state quantities are prepared by simulations, so that it is possible to easily collect a needed amount of operating state quantities as compared to a case in which the operating state quantities are collected by operating the air conditioner 1.

In the present embodiment, a case has been described as an example in which the estimation model 45A and the determination model 46A are generated by the server 120 or the control circuit 19; however, a user may calculate the estimation model 45A and the determination model 46A from the simulation result. Furthermore, in the present embodiment, a case has been described as an example in which each of the estimation models is generated by using the multiple regression analysis method; however, it may be possible to generate the estimation model by using support vector regression (SVR), a neural network (NN), or the like of a machine learning model that can perform a general regression analysis method. At this time, at the time of selection of a feature value, instead of the P value and the correction value R2 that are used in the multiple regression analysis method, a general method (a forward feature selection method, a backward feature elimination, or the like) for selecting a feature value such that accuracy of the estimation model is improved may be used.

Furthermore, in the present embodiment, a case has been described as an example in which the determination model 46A is generated by using the Kernel density estimation method; however, the example is not limited to the Kernel density estimation method as long as a non-linear analysis method is used, and appropriate modifications are possible.

Furthermore, in the present embodiment, a case has been described as an example in which the air conditioner 1 is constituted such that the one or more indoor units 3 are connected to the single outdoor unit 2; however, the embodiment may also be applicable to the air conditioner 1 in which the one or more indoor units 3 are connected to the two or more outdoor units 2.

In the first embodiment, a case has been described as an example in which the simulation result of each of the operating state quantities is obtained at the design stage of the air conditioner 1, and the control circuit 19 stores therein the estimation model 45A and the determination model 46A that are obtained by causing an information processing apparatus, such as a server, having a learning function to learn a simulation result. However, it may be possible to provide a server that is connected to the air conditioner 1 via a communication network, and the server may generate and transmit the estimation model 45A and the determination model 46A to the air conditioner 1. In addition, the air conditioner 1 may store the estimation model 45A and the determination model 46A that are received from the server in the control circuit 19.

The refrigerant circuit 6 is constituted such that at least the one indoor unit 3 that is connected to at least the one outdoor unit 2 is connected by a refrigerant pipe. Therefore, the estimation model 45A is able to estimate the refrigerant shortage rate by using the detection value of the first feature value of the single representative outdoor unit 2 among at least the one outdoor unit 2 and the detected value of the first feature value of the single representative indoor unit 3 among at least the one indoor unit 3. In addition, it is assumed that the representative outdoor unit 2 is selected from at least the one outdoor unit 2 that is in operation on the basis of an arbitrary rule, and the representative indoor unit 3 is also selected from at least the one indoor unit 3 that is in operation on the basis of an arbitrary rule. The arbitrary rule is, for example, ascending order of identification numbers that are assigned to the devices.

Each of the components in the units illustrated in the drawings is not always physically configured as illustrated in the drawings. In other words, the specific shape of a separate or integrated unit is not limited to the drawings; however, all or part of the unit can be configured by functionally or physically separating or integrating any of the units depending on various kinds of loads or use conditions.

Furthermore, all or any part of various processing functions performed by each unit may also be executed by a central processing unit (CPU) (or, a microcomputer, such as a micro processing unit (MPU) or a micro controller unit (MCU)). In addition, all or any part of various processing functions may also be, of course, executed by programs analyzed and executed by the CPU (or the microcomputer, such as the MPU or the MCU), or executed by hardware by wired logic.

Furthermore, in each of the embodiments described above, it is assumed that the refrigerant shortage rate corresponds to an amount of reduction from a defined amount in the case where the defined amount of the refrigerant that is filled is defined as 100%. Instead of this, it may be possible to estimate the refrigerant shortage rate by using the method described in the present embodiment immediately after the defined amount of the refrigerant is filled in the refrigerant circuit 6, and set the obtained estimation result as 100%. For example, if the refrigerant shortage rate that is estimated immediately after the defined amount of refrigerant is filled in the refrigerant circuit 6 is 90%, that is, if it is estimated that the amount of refrigerant that is currently filled in the refrigerant circuit 6 is smaller than the defined amount by 10%, it may be possible to set the amount of refrigerant that is smaller than the defined amount by as 100%. By adjusting the refrigerant amount that is set as 100% to the estimation result, it is possible to more accurately estimate the refrigerant shortage rate.

REFERENCE SIGNS LIST

- 1 air conditioner
- 2 outdoor unit
- 3 indoor unit
- 41 acquisition unit
- 44 control unit
- 45 estimation unit
- 45A estimation model
- 46 determination unit
- 46A determination model
- 46B cooling time determination model
- 46C heating time determination model
- 100 air conditioning system
- 120 server
- 121 generation unit
- 121A communication unit
- 122 estimation unit
- 123 determination unit

Claims

1. An air conditioning system comprising: an air conditioner that includes a refrigerant circuit that is constituted to have a structure in which at least one indoor unit is connected to an outdoor unit by a refrigerant pipe and a predetermined amount of refrigerant is filled; anda server that is communicably connected to the air conditioner, whereinthe air conditioner includes an first memory; and first processing circuitry coupled to the first memory and configured to:detect a state quantity related to control of the air conditioner,acquire a detection value that has been detected by the detecting, andtransmit the detection value that has been acquired by the acquiring to the server, andthe server includes a second memory; and second processing circuitry coupled to the second memory and configured to:receive the detection value from the air conditioner,estimate a remaining refrigerant amount of the refrigerant that remains in the refrigerant circuit by using a detection value of a first feature value in a case where a state quantity related to an amount of the refrigerant that is filled in the refrigerant circuit is indicated by the first feature value, anddetermine whether or not the detection value of the first feature value is a detection value that is to be used to estimate the remaining refrigerant amount performed by the estimating.
2. The air conditioning system according to claim 1, wherein, in a case where, from among the state quantities, state quantities that include at least one state quantity that is included in the first feature value and at least one state quantity that is not included in the first feature value are indicated by a second feature value, the determining determines, by using a detection value of the second feature value, whether or not the detection value of the first feature value is the detection value that is to be used to estimate the remaining refrigerant amount.
3. The air conditioning system according to claim 2, wherein a number of state quantities included in the second feature value is larger than the number of state quantities included in the first feature value.
4. The air conditioning system according to claim 2, wherein the estimating includes an estimation model that is generated by using the first feature value, andestimates the remaining refrigerant amount by applying the detection value of the first feature value to the estimation model, andthe determining includes a determination model that is generated by using the second feature value, anddetermines, by applying the detection value of the second feature value to the determination model, whether or not the detection value of the first feature value is to be used to estimate the remaining refrigerant amount performed by the estimation unit estimating.
5. The air conditioning system according to claim 4, wherein the determination model calculates, by adopting the second feature value that is used to generate the determination model as a normal sample value, an outlier that indicates a degree of deviation from the normal sample value related to the detection value of the second feature value from among the detection values acquired by the acquiring, and,when an absolute value of the calculated outlier is larger than a predetermined threshold, the determining does not estimate the remaining refrigerant amount performed by the estimating by using the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value, and,when the absolute value of the calculated outlier is less than the predetermined threshold, the determining estimates the remaining refrigerant amount performed by the estimating by using the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value.
6. The air conditioning system according to claim 1, wherein, before the remaining refrigerant amount is estimated by the estimating, the determining determines whether or not the detection value of the first feature value is to be used.
7. The air conditioning system according to claim 4, wherein, before the remaining refrigerant amount is estimated by the estimating, the determining determines whether or not the detection value of the first feature value is to be used.
8. The air conditioning system according to claim 4, wherein the second feature value that is used to generate the determination model is a value that is obtained from a result of a simulation of an operation of the refrigerant circuit when the operation of the refrigerant circuit is normal and only the remaining refrigerant amount has been changed.
9. The air conditioning system according to claim 4, wherein the outdoor unit includes a compressor,the detecting includes a suction temperature sensor that detects a suction temperature that is a temperature of the refrigerant that is sucked into the compressor, andthe second feature value includes a rotation speed of the compressor and the suction temperature.
10. The air conditioning system according to claim 9, wherein the determination model includes a heating time determination model that is used when the air conditioner performs a heating operation, anda cooling time determination model that is used when the air conditioner performs a cooling operation, andthe second feature value that is used by the heating time determination model is different from the second feature value that is used by the cooling time determination model except for the rotation speed of the compressor and the suction temperature.
11. The air conditioning system according to claim 10, wherein the outdoor unit further includes an outdoor heat exchanger, andan outdoor unit expansion valve,the detecting further includes a discharge temperature sensor that detects a discharge temperature that is a temperature of the refrigerant that is discharged from the compressor,a discharge pressure sensor that detects a discharge pressure that is a pressure of the refrigerant that is discharged from the compressor, anda suction pressure sensor that detects a suction pressure that is a pressure of the refrigerant that is sucked into the compressor,the discharge temperature, the suction pressure, a low pressure saturation temperature that is calculated by using the suction pressure, and a degree of opening of the outdoor unit expansion valve are included as the second feature value that is used for the heating time determination model, andthe heat exchange outlet temperature of the outdoor heat exchanger, a high pressure saturation temperature that is calculated by using the discharge pressure, and the discharge pressure are included as the second feature value that is used for the cooling time determination model.
12. The air conditioning system according to claim 4, wherein the indoor unit includes an indoor heat exchanger, andan indoor unit expansion valve,the detecting includes a refrigerant temperature sensor that detects an indoor unit side heat exchange inlet temperature that is a temperature of the refrigerant flowing into the indoor heat exchanger at the time of a heating operation, andan indoor unit side heat exchange outlet temperature that is a temperature of the refrigerant flowing out from the indoor heat exchanger at the time of the heating operation, andthe second feature value includes the indoor unit side heat exchange inlet temperature,the indoor unit side heat exchange outlet temperature, anda degree of opening of the indoor unit expansion valve.
13. The air conditioning system according to claim 4, wherein the estimation model is generated by using linear analysis, andthe determination model is generated by using non-linear analysis.
14. The air conditioning system according to claim 5, wherein the estimation model is generated by using linear analysis, andthe determination model is generated by using non-linear analysis.
15. A refrigerant amount estimation method that is implemented by an air conditioning system that includes an air conditioner that includes a refrigerant circuit that is constituted to have a structure in which at least one indoor unit is connected to an outdoor unit by a refrigerant pipe and a predetermined amount of refrigerant is filled, anda server that is communicably connected to the air conditioner, the refrigerant amount estimation method for the air conditioning system comprising:detecting, performed by first processing circuitry included in the air conditioner, a state quantity related to control of the air conditioner;acquiring, performed by the first processing circuitry included in the air conditioner, a detection value of the detected state quantity;transmitting, performed by the first processing circuitry included in the air conditioner, the acquired detection value to the server;receiving, performed by second processing circuitry included in the server, the detection value from the air conditioner;determining, performed by the second processing circuitry included in the server, whether or not a detection value of a first feature value is a detection value that is to be used to estimate a remaining refrigerant amount in a case where a state quantity related to an amount of the refrigerant that is filled in the refrigerant circuit is indicated by the first feature value; andestimating, performed by an the second processing circuitry included in the server, the remaining refrigerant amount of the refrigerant that remains in the refrigerant circuit by using the detection value of the first feature value.
16. The refrigerant amount estimation method for the air conditioning system according to claim 15, wherein, in a case where, from among the state quantities, state quantities that include at least one state quantity that is included in the first feature value and at least one state quantity that is not included in the first feature value are indicated by a second feature value, the determining includes determining, performed by the determining, by using a detection value of the second feature value, whether or not the detection value of the first feature value is the detection value that is to be used to estimate the remaining refrigerant amount.
17. The refrigerant amount estimation method for the air conditioning system according to claim 16, further comprising: inputting, performed by the determining, the detection value of the second feature value to a determination model that has been generated by using the second feature value;calculating, performed by the determining, by adopting the second feature value that is used to generate the determination model as a normal sample value, an outlier that indicates a degree of deviation from the normal sample value related to the detection value of the second feature value from among the detection values acquired by the acquiring; anddetermining, performed by the determining, when an absolute value of the outlier is larger than a predetermined threshold, that the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value is not to be used to estimate the remaining refrigerant amount.
18. The refrigerant amount estimation method for the air conditioning system according to claim 17, wherein the second feature value that is used to generate the determination model is a value that is obtained from a result of a simulation of an operation of the refrigerant circuit when the operation of the refrigerant circuit is normal and only the remaining refrigerant amount has been changed.
19. The refrigerant amount estimation method for the air conditioning system according to claim 17, further comprising determining, performed by the determining, when the absolute value of the outlier is less than the predetermined threshold, that the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value is to be used to estimate the remaining refrigerant amount.
20. An air conditioner that includes a refrigerant circuit that is constituted to have a structure in which at least one indoor unit is connected to an outdoor unit by a refrigerant pipe and a predetermined amount of refrigerant is filled, the air conditioner comprising: an first memory; and first processing circuitry coupled to the first memory and configured to:detect a state quantity related to control of the air conditioner;acquire a detection value that has been detected by the detecting;estimate a remaining refrigerant amount of the refrigerant that remains in the refrigerant circuit by using a detection value of a first feature value in a case where a state quantity related to an amount of the refrigerant that is filled in the refrigerant circuit is indicated by the first feature value; anddetermine whether or not the detection value of the first feature value is a detection value that is to be used to estimate the remaining refrigerant amount performed by the estimating.
21. The air conditioner according to claim 20, wherein, in a case where, from among the state quantities, state quantities that include at least one state quantity that is included in the first feature value and at least one state quantity that is not included in the first feature value are indicated by a second feature value, the determining determines, by using a detection value of the second feature value, whether or not the detection value of the first feature value is the detection value that is to be used to estimate the remaining refrigerant amount.
22. The air conditioner according to claim 21, wherein a number of state quantities included in the second feature value is larger than the number of state quantities included in the first feature value.
23. The air conditioner according to claim 21, wherein the estimating includes an estimation model that is generated by using the first feature value, andestimates the remaining refrigerant amount by applying detection value of the first feature value to the estimation model, andthe determining includes a determination model that is generated by using the second feature value, anddetermines, by applying the detection value of the second feature value to the determination model, whether or not the detection value of the first feature value is to be used to estimate the remaining refrigerant amount performed by the estimating.
24. The air conditioner according to claim 23, wherein the determination model calculates, by adopting the second feature value that is used to generate the determination model as a normal sample value, an outlier that indicates a degree of deviation from the normal sample value related to the detection value of the second feature value from among the detection values acquired by the acquiring, and,when an absolute value of the calculated outlier is larger than a predetermined threshold, the determining does not estimate the remaining refrigerant amount performed by the estimating by using the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value, and,when the absolute value of the calculated outlier is less than the predetermined threshold, the determining estimates the remaining refrigerant amount performed by the estimating by using the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value.
25. The air conditioner according to claim 20, wherein, before the remaining refrigerant amount is estimated by the estimating, the determining determines whether or not the detection value of the first feature value is to be used.
26. The air conditioner according to claim 23, wherein, before the remaining refrigerant amount is estimated by the estimating, the determining determines whether or not the detection value of the first feature value is to be used.
27. The air conditioner according to claim 23, wherein the second feature value that is used to generate the determination model is a value that is obtained from a result of a simulation of an operation of the refrigerant circuit when the operation of the refrigerant circuit is normal and only the remaining refrigerant amount has been changed.
28. The air conditioner according to claim 23, wherein the outdoor unit includes a compressor,the detecting includes a suction temperature sensor that detects a suction temperature that is a temperature of the refrigerant that is sucked into the compressor, andthe second feature value includes a rotation speed of the compressor and the suction temperature.
29. The air conditioner according to claim 28, wherein the determination model includes a heating time determination model that is used when the air conditioner performs a heating operation, anda cooling time determination model that is used when the air conditioner performs a cooling operation, andthe second feature value that is used by the heating time determination model is different from the second feature value that is used by the cooling time determination model except for the rotation speed of the compressor and the suction temperature.
30. The air conditioner according to claim 29, wherein the outdoor unit further includes an outdoor heat exchanger, andan outdoor unit expansion valve,the detecting further includes a discharge temperature sensor that detects a discharge temperature that is a temperature of the refrigerant that is discharged from the compressor,a discharge pressure sensor that detects a discharge pressure that is a pressure of the refrigerant that is discharged from the compressor, anda suction pressure sensor that detects a suction pressure that is a pressure of the refrigerant that is sucked into the compressor,the discharge temperature, the suction pressure, a low pressure saturation temperature that is calculated by using the suction pressure, and a degree of opening of the outdoor unit expansion valve are included as the second feature value that is used for the heating time determination model, andthe heat exchange outlet temperature of the outdoor heat exchanger, a high pressure saturation temperature that is calculated by using the discharge pressure, and the discharge pressure are included as the second feature value that is used for the cooling time determination model.
31. The air conditioner according to claim 23, wherein the indoor unit includes an indoor heat exchanger, andan indoor unit expansion valve,the detecting includes a refrigerant temperature sensor that detects an indoor unit side heat exchange inlet temperature that is a temperature of the refrigerant flowing into the indoor heat exchanger at the time of a heating operation, andan indoor unit side heat exchange outlet temperature that is a temperature of the refrigerant flowing out from the indoor heat exchanger at the time of the heating operation, andthe second feature value includes the indoor unit side heat exchange inlet temperature,the indoor unit side heat exchange outlet temperature, anda degree of opening of the indoor unit expansion valve.
32. The air conditioner according to claim 23, wherein the estimation model is generated by using linear analysis, andthe determination model is generated by using non-linear analysis.
33. The air conditioner according to claim 24, wherein the estimation model is generated by using linear analysis, andthe determination model is generated by using non-linear analysis.
34. A refrigerant amount estimation method for estimating a remaining refrigerant amount in an air conditioner that includes a refrigerant circuit that is constituted to have a structure in which at least one indoor unit is connected to an outdoor unit by a refrigerant pipe and a predetermined amount of refrigerant is filled or estimating a remaining refrigerant amount in an air conditioning system that includes the air conditioner, the refrigerant amount estimation method for the air conditioner comprising: detecting a state quantity related to control of the air conditioner;acquiring a detection value of the detected state quantity;determining whether or not a detection value of a first feature value is a detection value that is to be used to estimate the remaining refrigerant amount in a case where a state quantity related to an amount of the refrigerant that is filled in the refrigerant circuit is indicated by the first feature value; andestimating the remaining refrigerant amount of the refrigerant that remains in the refrigerant circuit by using the detection value of the first feature value.
35. The refrigerant amount estimation method for the air conditioner according to claim 34, wherein, in a case where, from among the state quantities, state quantities that include at least one state quantity that is included in the first feature value and at least one state quantity that is not included in the first feature value are indicated by a second feature value, the determining includes determining, by using a detection value of the second feature value, whether or not the detection value of the first feature value is the detection value that is to be used to estimate the remaining refrigerant amount.
36. The refrigerant amount estimation method for the air conditioner according to claim 35, wherein the determining further includes calculating, by adopting the second feature value as a normal sample value, an outlier that indicates a degree of deviation from the normal sample value related to the detection value of the second feature value from among the acquired detection values, anddetermining, when an absolute value of the outlier is larger than a predetermined threshold, that the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value is not to be used to estimate the remaining refrigerant amount.
37. The refrigerant amount estimation method for the air conditioner according to claim 35, wherein the second feature value that is used at the determining is a value that is obtained from a result of a simulation of an operation of the refrigerant circuit when the operation of the refrigerant circuit is normal and only the remaining refrigerant amount has been changed.
38. The refrigerant amount estimation method for the air conditioner according to claim 36, further comprising determining, when the absolute value of the outlier is less than the predetermined threshold, that the detection value of the first feature value that is acquired at the same time as the detection value of the second feature value is to be used to estimate the remaining refrigerant amount.

Priority Claims (1)

Number	Date	Country	Kind
2021-062276	Mar 2021	JP	national

PCT Information

Filing Document	Filing Date	Country	Kind
PCT/JP2022/007461	2/24/2022	WO

AIR CONDITIONING SYSTEM, REFRIGERANT AMOUNT ESTIMATION METHOD FOR AIR CONDITIONING SYSTEM, AIR CONDITIONER, AND REFRIGERANT AMOUNT ESTIMATION METHOD FOR AIR CONDITIONER

Information

Publication Number

Date Filed

Date Published

Inventors

Original Assignees

CPC

International Classifications

Abstract

Description

Claims

Priority Claims (1)

PCT Information