The present invention relates to an apparatus, a method, and a program for estimating an amount of a refrigerant.
Conventionally, a method for detecting loss (leakage of refrigerant) of a filled amount of refrigerant of a cooling system based on an index value of a refrigerant amount filled (hereinafter, also referred to as a refrigerant amount index value) is disclosed. Specifically, PTL 1 discloses calculating a real-time air side temperature difference across an evaporator; calculating a first air side temperature difference across the evaporator by applying an algorithm having a first T-Map representative of normal operating conditions; and taking an action if the real-time air side temperature difference is less than the first air side temperature difference (PTL 1, paragraph [0004]).
[PTL 1]
Japanese Translation of PCT International Application Publication No. JP-T-2018-533718
However, in a prediction using a map as described in PTL 1, an argument of the parameter that affects the refrigerant amount index value other than change in a refrigerant amount is a discrete value. Accordingly, the predicted value of the refrigerant amount index value predicted by the map is a discrete value. Therefore, when the step width of the argument is large, the accuracy of the prediction by the map is poor, and when the step width is small in order to increase the accuracy of the prediction, the amount of data of the map is large. Moreover, when the types of parameters for the arguments increase, the map is multidimensional, and the amount of data is large, making implementation difficult. The present disclosure is intended to facilitate the determination of the refrigerant amount.
The 1st aspect of the present disclosure is: a refrigerant amount determining device including an operation data acquiring unit configured to acquire operation data of an air conditioning system; a calculating unit configured to calculate a refrigerant amount index value from the operation data acquired; an inferring unit configured to infer information regarding correction of the refrigerant amount index value using a correction model and at least one of the acquired operation data or the calculated refrigerant amount index value; and a determining unit configured to determine a refrigerant amount of the air conditioning system based on the information regarding correction of the refrigerant amount index value.
According to a 1st aspect of the present disclosure, an argument of the parameter that affects the refrigerant amount index value and a predicted value are continuous values, providing easy implementation even when the types of parameters for the arguments increase.
A 2nd aspect of the present disclosure is: the refrigerant amount determining device according to the 1st aspect, wherein the inferring unit is configured to infer a corrected refrigerant amount index value in which the calculated refrigerant amount index value is corrected, using the calculated refrigerant amount index value and the correction model, and the determining unit is configured to determine the refrigerant amount of the air conditioning system based on the corrected refrigerant amount index value.
A 3rd aspect of the present disclosure is: the refrigerant amount determining device according to the 1st aspect, wherein the operation data includes first operation data and second operation data, the first operation data and the second operation data being at least partially different, or the first operation data and the second operation data being at least partially identical, the calculating unit is configured to calculate the refrigerant amount index value from the first operation data, the inferring unit is configured to infer a corrected refrigerant amount index value in which the calculated refrigerant amount index value is corrected using the second operation data, the calculated refrigerant amount index value, and the correction model, and the determining unit is configured to determine the refrigerant amount of the air conditioning system based on the corrected refrigerant amount index value.
A 4th aspect of the present disclosure is: the refrigerant amount determining device according to the 1st aspect, wherein the operation data includes first operation data and second operation data, the first operation data and the second operation data being at least partially different, or the first operation data and the second operation data being at least partially identical, the calculating unit is configured to calculate the refrigerant amount index value from the first operation data, the inferring unit is configured to infer a corrected range of the refrigerant amount index value using the second operation data and the correction model, and the determining unit is configured to determine the refrigerant amount of the air conditioning system based on the calculated refrigerant amount index value and the corrected range of the refrigerant amount index value.
A 5th aspect of the present disclosure is: the refrigerant amount determining device according to the 1st aspect, wherein the operation data includes first operation data and second operation data, the first operation data and the second operation data being at least partially different, or the first operation data and the second operation data being at least partially identical, the calculating unit is configured to calculate the refrigerant amount index value from the first operation data, the inferring unit is configured to infer information for specifying a corrected refrigerant amount index value in which the calculated refrigerant amount index value is corrected using the second operation data and the correction model, and the determining unit is configured to determine the refrigerant amount of the air conditioning system based on the calculated refrigerant amount index value and the information for specifying the corrected refrigerant amount index value.
A 6th aspect of the present disclosure is: the refrigerant amount determining device according to the 1st aspect, wherein the operation data includes first operation data and second operation data, the first operation data and the second operation data being at least partially different, or the first operation data and the second operation data being at least partially identical, the calculating unit is configured to calculate the refrigerant amount index value from the first operation data, the inferring unit is configured to infer a corrected difference or ratio between the calculated refrigerant amount index value and a predicted value of the refrigerant amount index value predicted from the second operation data using the second operation data, the calculated refrigerant amount index value, and the correction model, and the determining unit is configured to determine the refrigerant amount of the air conditioning system based on the corrected difference or ratio.
A 7th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 2nd to 6th aspects, wherein one or more refrigerant amount index value and one or more correction model is used.
An 8th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 7th aspects, wherein the correction model is a model learned by associating the operation data at at least one of normal operation and abnormal operation and the refrigerant amount index value with each other.
A 9th aspect of the present disclosure is: the refrigerant amount determining device according to the 8th aspect, wherein the operation data at at least one of normal operation and abnormal operation includes at least one of measured data and pseudo data.
A 10th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 9th aspects, further including an output correction unit that is configured to correct the information regarding correction of the refrigerant amount index value.
An 11th aspect of the present disclosure is: the refrigerant amount determining device according to the 10th aspect, wherein the output correction unit is configured to correct an offset amount between: the refrigerant amount index value when the refrigerant amount is a designed value; and the measured value of the refrigerant amount index value.
A 12th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 9th aspects, further including an input correction unit that is configured to correct the operation data.
A 13th aspect of the present disclosure is: the refrigerant amount determining device according to the 12th aspect, wherein the input correction unit is configured to increase or decrease an acquisition interval of the operation data according to a number of pieces of the operation data.
A 14th aspect of the present disclosure is: the refrigerant amount determining device according to the 12th aspect, wherein the operation data includes at least one of measured data or pseudo data, and the input correction unit is configured to create pseudo data of the operation data.
A 15th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 9th aspects, further including: an output correction unit that is configured to correct the information regarding correction of the refrigerant amount index value; and an input correction unit that is configured to correct the operation data.
A 16th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 15th aspects, further including an outputting unit that is configured to output a determination result of at least one of a value for determining the refrigerant amount, a category for determining the refrigerant amount, or both a category for determining the refrigerant amount and a reliability thereof.
A 17th aspect of the present disclosure is: the refrigerant amount determining device according to the 16th aspect, wherein the determining unit is configured to perform the determination using a determination result output by the outputting unit.
An 18th aspect of the present disclosure is: the refrigerant amount determining device according to the 16th aspect, further including a learned model acquiring unit that is configured to acquire a correction model that is a result of learning in which the operation data and the refrigerant amount index value are associated with each other.
A 19th aspect of the present disclosure is: the refrigerant amount determining device according to the 18th aspect, wherein the learned model acquiring unit is configured to acquire an optimum correction model using the determination result output by the outputting unit.
A 20th aspect of the present disclosure is: the refrigerant amount determining device according to the 16th aspect, further including a learning unit that is configured to learn by associating the operation data and the refrigerant amount index value with each other.
A 21st aspect of the present disclosure is: the refrigerant amount determining device according to the 20th aspect, wherein the learning unit is configured to relearn using the determination result output by the outputting unit.
A 22nd aspect of the present disclosure is: the refrigerant amount determining device according to the 20th aspect, wherein the learning unit is configured to change the learning data using the determination result output by the outputting unit and relearn the correction model.
A 23rd aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 22nd aspects, wherein the correction model is a model learned by associating external sensor data, the operation data, and a refrigerant amount index with one another, the operation data acquiring unit is configured to further acquire external sensor data, and the inferring unit is configured to infer the information regarding correction of the refrigerant amount index value using the acquired external sensor data, the operation data, and the correction model.
A 24th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 22nd aspects, wherein the correction model is a model learned by associating image data, the operation data, and a refrigerant amount index with one another, the operation data acquiring unit is configured to further acquire image data, and the inferring unit is configured to infer the information regarding correction of the refrigerant amount index value using the acquired image data, the operation data, and the correction model.
A 25th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 22nd aspects, wherein the correction model is a model learned by associating installation status data of the air conditioning system, the operation data, and a refrigerant amount index with one another, the operation data acquiring unit is configured to further acquire installation status data, and the inferring unit is configured to infer the information regarding correction of the refrigerant amount index value using the acquired installation status data, the operation data, and the correction model.
A 26th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 25th aspects, wherein the operation data includes at least one of outdoor temperature, a rotation speed of a compressor, an opening degree of an expansion valve of a subcooling heat exchanger, and a current value of the compressor.
A 27th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 26th aspects, wherein the refrigerant amount index value includes at least one of a degree of subcooling at an outdoor heat exchanger outlet; a degree of superheating in suction of a compressor; a degree of superheating in discharge of the compressor; and a value based on the degree of subcooling at the outdoor heat exchanger outlet, the degree of superheating in suction of the compressor, or the degree of superheating in discharge of the compressor.
A 28th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 27th aspects, wherein the refrigerant amount index value includes at least one of a degree of subcooling at a subcooling heat exchanger outlet and a value based on the degree of subcooling at the subcooling heat exchanger outlet.
A 29th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 26th aspects, wherein the refrigerant amount index value includes at least one of a degree of subcooling at an indoor heat exchanger outlet and a value based on the degree of subcooling at the indoor heat exchanger outlet, the degree of subcooling at the indoor heat exchanger outlet is any one of at least one of the degree of subcooling of indoor heat exchangers; an average value of the indoor heat exchangers; or a degree of subcooling at an indoor or outdoor confluence of the indoor heat exchangers.
A 30th aspect of the present disclosure is: the refrigerant amount determining device according to the 27th or 28th aspect, wherein the refrigerant amount index value is a combination of a degree of subcooling at an indoor heat exchanger outlet of a simultaneous cooling and heating operation device in a heating operation mode and a degree of subcooling at an outdoor heat exchanger outlet, functioning as condenser, of the simultaneous cooling and heating operation device.
A 31st aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 30th aspects, wherein the operation data includes at least one of:
A 32nd aspect of the present disclosure is: the refrigerant amount determining device according to the 29th or 30th aspect, wherein the operation data includes at least one of a number of times of defrosting, or duration of defrosting.
A 33rd aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 32nd aspects, wherein the determining unit is configured to determine the refrigerant amount of the air conditioning system based on both a difference or ratio between: the calculated refrigerant amount index value; and an inferred predicted value of the refrigerant amount index value at a normal operation, and a difference or ratio between: the refrigerant amount index value calculated from an operating condition when the operation data for calculating the refrigerant amount index value was acquired and from a past operation data that was acquired when an operating condition was in a predetermined range; and an inferred predicted value of the refrigerant amount index value at a normal operation.
A 34th aspect of the present disclosure is: the refrigerant amount determining device according to the 33rd aspect, wherein the operating condition is an outdoor temperature.
A 35th aspect of the present disclosure is: the refrigerant amount determining device according to any one of the 1st to 34th aspects, wherein the determining unit is configured to determine a ratio of a leakage amount to an appropriate amount of the refrigerant of the air conditioning system based on a difference or ratio between the calculated refrigerant amount index value and an inferred predicted value of the refrigerant amount index value at a normal operation.
A 36th aspect of the present disclosure is:
a method including:
acquiring operation data of an air conditioning system;
calculating a refrigerant amount index value from the operation data acquired;
correcting the refrigerant amount index value using the acquired operation data and a correction model; and
determining a refrigerant amount of the air conditioning system based on the corrected refrigerant amount index value.
A 37th aspect of the present disclosure is:
a program for causing a refrigerant amount determining device to function as:
an operation data acquiring unit configured to acquire operation data of an air conditioning system;
a calculating unit configured to calculate a refrigerant amount index value from the operation data acquired;
an inferring unit configured to correct the refrigerant amount index value using the acquired operation data and a correction model; and
a determining unit configured to determine a refrigerant amount of the air conditioning system based on the corrected refrigerant amount index value.
Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings.
Referring to
<Overall Configuration (For Cooling Operation)>
In the example of
«Outdoor Unit»
On the outdoor unit 200 side, the outdoor heat exchanger 201, the compressor 202, the subcooling heat exchanger 203, the subcooling heat exchanger expansion valve (bypass circuit) 204, and the outdoor unit main expansion valve (main refrigerant circuit) 205 are connected to the pipe. The outdoor unit 200 includes a variety of sensors (for example, temperature sensors (for example, thermistors) (1), (3), (4), (6), and (7), and pressure sensors (2) and (5), and the like).
«Indoor Unit»
On the indoor unit 300 side, the indoor heat exchanger 301 and the indoor heat exchanger expansion valve 302 are connected to the pipe. The indoor unit 300 includes a variety of sensors (for example, temperature sensors (for example, thermistors) (8) and (9), and the like).
«Refrigerant Amount Determining Device»
The refrigerant amount determining device 400 is a device for determining the refrigerant amount of the air conditioning system 100. The refrigerant amount determining device 400 will be described in detail below with reference to
The refrigerant amount determining device 400 may be implemented on a device (for example, a computer installed in the same building or the like as the air conditioning system 100, or a cloud server remote from the air conditioning system 100) communicatively connected with the air conditioning system 100. The refrigerant amount determining device 400 may be implemented as part of the air conditioning system 100 (for example, installed in the outdoor unit 200 or in the indoor unit 300).
<Overall Configuration (For Heating Operation)>
In the example of
«Outdoor Unit»
On the outdoor unit 200 side, the outdoor heat exchanger 201, the compressor 202, the subcooling heat exchanger 203, the subcooling heat exchanger expansion valve (bypass circuit) 204, and the outdoor unit main expansion valve (main refrigerant circuit) 205 are connected to the pipe. The outdoor unit 200 includes a variety of sensors (for example, temperature sensors (for example, thermistors) (1), (3), (4), (6), and (7), and pressure sensors (2) and (5), and the like).
«Indoor Unit»
On the indoor unit 300 side, the indoor heat exchanger 301 and the indoor heat exchanger expansion valve 302 are connected to the pipe. The indoor unit 300 includes a variety of sensors (for example, temperature sensors (for example, thermistors) (8) and (9), and the like).
«Refrigerant Amount Determining Device»
The refrigerant amount determining device 400 is a device for determining the refrigerant amount of the air conditioning system 100. The refrigerant amount determining device 400 will be described in detail below with reference to
The refrigerant amount determining device 400 may be implemented on a device (for example, a computer installed in the same building or the like as the air conditioning system 100, or a cloud server remote from the air conditioning system 100) communicatively connected with the air conditioning system 100. The refrigerant amount determining device 400 may be implemented as part of the air conditioning system 100 (for example, installed in the outdoor unit 200 or in the indoor unit 300).
<Overall Configuration (For Simultaneous Cooling and Heating Operation)>
The present disclosure may be applicable not only to cooling operation and heating operation, but also to simultaneous cooling and heating operation. Hereinafter, the simultaneous cooling and heating operation will be described with reference to
«Refrigerant Amount Determining Device»
The refrigerant amount determining device 400 is a device for determining the refrigerant amount of the air conditioning system 100. The refrigerant amount determining device 400 will be described in detail below with reference to
The refrigerant amount determining device 400 may be implemented on a device (for example, a computer installed in the same building or the like as the air conditioning system 100, or a cloud server remote from the air conditioning system 100) communicatively connected with the air conditioning system 100. The refrigerant amount determining device 400 may be implemented as part of the air conditioning system 100 (for example, installed in the outdoor unit 200 or in the indoor unit 300).
<Hardware Configuration of Refrigerant Amount Determining Device>
The refrigerant amount determining device 400 may include an auxiliary storage device 4, a display device 5, an operating device 6, and an interface (I/F) device 7. Each of the hardware of the refrigerant amount determining device 400 is connected to each other via a bus 8.
The CPU 1 is an arithmetic device which executes various programs installed in the auxiliary storage device 4.
The ROM 2 is a non-volatile memory. The ROM 2 functions as a main storage device for storing various programs, data, and the like necessary for the CPU 1 to execute various programs installed in the auxiliary storage device 4. Specifically, The ROM 2 functions as a main storage device for storing a boot program such as Basic Input/Output System (BIOS) and Extensible Firmware Interface (EFI), and the like.
The RAM 3 is a volatile memory such as Dynamic Random Access Memory (DRAM) and Static Random Access Memory (SRAM). The RAM 3 functions as a main storage device that provides a workspace deployed when various programs installed in the auxiliary storage device 4 are executed by the CPU 1.
The auxiliary storage device 4 is an auxiliary storage device that stores various programs and information used when the various programs are executed.
The display device 5 is a display device that displays the internal state and the like of the refrigerant amount determining device 400.
The operating device 6 is an input device in which an administrator of the refrigerant amount determining device 400 inputs various instructions to the refrigerant amount determining device 400.
The I/F device 7 is a communication device that connects to various sensors and networks and communicates with other terminals.
<Function Block of Refrigerant Amount Determining Device>
The operation data acquiring unit 401 acquires operation data (that is, current operation data) of the air conditioning system 100 from various sensors (temperature sensors, pressure sensors, and the like) of the air conditioning system 100. The operation data of the air conditioning system 100 is data that may be acquired during operation of the air conditioning system 100.
The calculating unit 402 calculates the refrigerant amount index value from the operation data acquired by the operation data acquiring unit 401. The refrigerant amount index value is an indicative value of the refrigerant amount and correlates with the refrigerant amount (details will be described later).
The inferring unit 403 infers a predicted value of the refrigerant amount index value at the normal operation from the operation data (correlating with the refrigerant amount index value; details will be described later) acquired by the operation data acquiring unit 401 based on the result of learning (the learned model 406) in which the operation data at the normal operation and the refrigerant amount index value are associated with each other. Specifically, the inferring unit 403 inputs the operation data acquired by the operation data acquiring unit 401 to the learned model 406 to obtain an output of a predicted value of the refrigerant amount index value at the normal operation.
The determining unit 404 determines the refrigerant amount of the air conditioning system 100 based on the difference or ratio between the refrigerant amount index value calculated by the calculating unit 402 and the predicted value of the refrigerant amount index value at the normal operation inferred by the inferring unit 403 (details will be described later).
The outputting unit 405 outputs the result determined by the determining unit 404. For example, the outputting unit 405 informs the administrator of the air conditioning system 100 of a leak of refrigerant.
The learned model 406 is the result of learning in which the operation data at the normal operation and the refrigerant amount index value are associated with each other, as described above.
The learned model acquiring unit 407 acquires the learned model 406 from the learning device 500.
Hereinafter, specific examples of «Refrigerant amount index value» and «Operation data for inferring predicted value of refrigerant amount index value at normal operation> will be described.
«Refrigerant Amount Index Value (Example 1: For Cooling Operation)»
For example, the refrigerant amount index value may include at least one of the values described below.
For example, the value based on the degree of subcooling at the outdoor heat exchanger outlet is a calculated value using the degree of subcooling at the outdoor heat exchanger outlet. For example, the calculated value using the degree of subcooling at the outdoor heat exchanger outlet is as described below.
For example, the value based on the degree of subcooling at the outdoor heat exchanger outlet is a value defined from a diagram of physical properties of refrigerant and refrigeration cycle (T-S and P-h diagram). Hereinafter, the value defined from the diagram of physical properties of refrigerant and refrigeration cycle (T-S and P-h diagram) will be described with reference to
Area A is the amount of change in one of exergy, enthalpy, and entropy in the process of the refrigerant being in the gas-liquid two-phase state in the condenser (201, 301) (in other words, the amount of change in one of exergy, enthalpy, and entropy in the process of the refrigerant changing from a saturated gas state to a saturated liquid state in the condenser (201, 301)).
Area B is the amount of change in one of exergy, enthalpy, and entropy in the process of the refrigerant being in the liquid monophase state in the condenser (201, 301) (in other words, the amount of change in one of exergy, enthalpy, and entropy in the process of the refrigerant being cooled from the saturated liquid state and reaching the condenser (201, 301) outlet).
«Refrigerant Amount Index Value (Example 2: For Cooling Operation)»
For example, the refrigerant amount index value may include at least one of the values described below, in addition to the refrigerant amount index value (Example 1) described above or in place of the degree of subcooling at the outdoor heat exchanger outlet of the refrigerant amount index value (Example 1) described above.
In the case of heating operation, the refrigerant amount index value may include, in place of the refrigerant amount index value (Example 1 and Example 2) described above, at least one of degree of subcooling at the indoor heat exchanger outlet and a value based on the degree of subcooling at the indoor heat exchanger outlet. The degree of subcooling at the indoor heat exchanger outlet may be any one of the following: at least one of the degree of subcooling of the indoor heat exchangers 301; an average value of the indoor heat exchangers 301; or the degree of subcooling at the indoor or outdoor confluence of the indoor heat exchangers 301.
«Refrigerant Amount Index Value (Example 3: For Simultaneous Cooling and Heating Operation)»
In the case of simultaneous cooling and heating operation, the refrigerant amount index value may include the value described below, in addition to the refrigerant amount index value (at least one of Example 1 or Example 2) described above.
For example, the operation data for inferring the predicted value of the refrigerant amount index value at the normal operation may include at least one of the values described below.
For example, the operation data for inferring the predicted value of the refrigerant amount index value at the normal operation may include at least one of the values described below, in addition to the operation data (Example 1) described above or in place of the operation data (Example 1) described above.
For example, the operation data for inferring the predicted value of the refrigerant amount index value at the normal operation may include at least one of the values described below, in addition to the operation data (Example 1 and Example 2) described above or in place of the operation data (Example 1 and Example 2) described above.
A combination of the refrigerant amount index value and the operation data for inferring the predicted value of the refrigerant amount index value at the normal operation will be described. For example, the refrigerant amount index value (Example 1) and the operation data for inferring the predicted value of the refrigerant amount index value at the normal operation (Example 1) may be used. For example, the refrigerant amount index value (Example 2) and the operation data for inferring the predicted value of the refrigerant amount index value at the normal operation (Example 1) may be used. For example, the refrigerant amount index value (Example 3) and the operation data for inferring the predicted value of the refrigerant amount index value at the normal operation (Example 1) may be used.
As illustrated in <Example 1>, the refrigerant amount determining device 400 may be implemented on a computer installed in, for example, the same building as the air conditioning system 100. The learning device 500 may also be implemented on a cloud server remote from the air conditioning system 100 and the refrigerant amount determining device 400.
As illustrated in <Example 2>, the refrigerant amount determining device 400 may be implemented as part of the air conditioning system 100 (for example, installed in the outdoor unit 200 or in the indoor unit 300). The learning device 500 may also be implemented on a cloud server remote from the air conditioning system 100 and the refrigerant amount determining device 400.
As illustrated in <Example 3>, the refrigerant amount determining device 400 and the learning device 500 may be implemented on a cloud server remote from the air conditioning system 100.
As illustrated in <Example 4>, the refrigerant amount determining device 400 and the learning device 500 may be implemented as part of the air conditioning system 100 (for example, installed in the outdoor unit 200 or in the indoor unit 300).
<Functional Block of Learning Device>
The teacher data acquiring unit 501 acquires teacher data. The teacher data acquiring unit 501 stores the acquired teacher data in the teacher data storage unit 502. The teacher data is the operation data and the refrigerant amount index value at the normal operation (that is, when the refrigerant in the air conditioning system 100 is in an appropriate amount (also referred to as an appropriate refrigerant amount)).
The teacher data storage unit 502 stores the teacher data.
The learning unit 503 extracts as learning data, from the operation data at the normal operation of the air conditioning system 100 in which the filled amount of the refrigerant is appropriate and no refrigerant leakage or other failure occurs, only the data of the item having a strong correlation with the refrigerant amount index value. The learning unit 503 performs machine learning by correlating each item with the refrigerant amount index value. The item having a strong correlation with the refrigerant amount index value is, for example, outdoor temperature, a rotation speed of the compressor 202, an opening degree of the expansion valve 204 of the subcooling heat exchanger, a current value of the compressor 202, and the like. As a result of learning using the learning data, a learned model is generated. When test data including the same items as the learned data is input to the learned model, the correlation between each item and the refrigerant amount index value are corrected and the predicted value of the refrigerant amount index value of the air conditioning system 100 at the time of acquiring the test data is output. The learned data need not necessarily be extracted from the operation data at the normal operation of the air conditioning system in which the refrigerant amount index value is to be predicted. The learned data may be extracted from the operation data at the normal operation of another air conditioning system, or may be extracted from the operation data at the normal operation of multiple air conditioning systems. To create learned models, machine learning algorithms such as random forests and support vector machines may be used.
<Refrigerant Leakage Determination Using Refrigerant Amount Index Value>
Hereinafter, the refrigerant leakage determination using the refrigerant amount index value in the refrigerant amount determining device 400 will be described with reference to
«When Input Items are Completely Corrected»
On the left side of
Suppose that refrigerant leakage occurred after August 2018 (square dots in
«When Input Items are Not Completely Corrected»
On the right side of
Suppose that refrigerant leakage occurred after August 2018 (square dots in
That is, when the input item is not completely corrected, the determining unit 404 determines the refrigerant amount of the air conditioning system based on both the difference or ratio between “the refrigerant amount index value calculated by the calculating unit 402” and “the predicted value of the refrigerant amount index value at the normal operation inferred by the inferring unit 403” or the difference or ratio between “the refrigerant amount index value calculated from the operating conditions when the operation data for calculating the refrigerant amount index value were obtained and from the past operation data that was obtained when the operating conditions were in a predetermined range” and “the predicted value of the refrigerant amount index value at the normal operation inferred by the inferring unit 403”. The determination using past operation data may be performed independently or after the determination not using past operation data is performed.
<Determination of Refrigerant Amount>
Hereinafter, a specific example of determination of the refrigerant amount will be described. As described above, the determining unit 404 determines the refrigerant amount of the air conditioning system 100 based on the difference or ratio between the refrigerant amount index value calculated by the calculating unit 402 and the predicted value of the refrigerant amount index value at the normal operation inferred by the inferring unit 403.
«Determination (Example 1)»
The determining unit 404 may determine the degree of increase or decrease of the refrigerant (for example, the degree of leakage of the refrigerant amount) from the appropriate refrigerant amount based on the difference or ratio between the refrigerant amount index value calculated by the calculating unit 402 and the predicted value of the refrigerant amount index value at the normal operation inferred by the inferring unit 403.
«Determination (Example 2)»
The determining unit 404 may determine the ratio of the leakage amount to the appropriate amount of refrigerant (for example, xx % of the refrigerant of the total refrigerant amount is leaked) based on the difference or ratio between the refrigerant amount index value calculated by the calculating unit 402 and the predicted value of the refrigerant amount index value at normal operation inferred by the inferring unit 403 and on the appropriate refrigerant amount of the air conditioning system 100. The determining unit 404 may be configured to determine the refrigerant amount (for example, “the current refrigerant amount is xx kg”).
<Processing Method>
Hereinafter, a determination process and a learning process according to an embodiment of the present disclosure will be described.
In Step 11 (S11), the operation data acquiring unit 401 acquires operation data of the air conditioning system 100 from various sensors (temperature sensors, pressure sensors, and the like) of the air conditioning system 100.
In Step 12 (S12), the calculating unit 402 calculates the refrigerant amount index value from the operation data acquired by the operation data acquiring unit 401 in S11.
In Step 13 (S13), the inferring unit 403 infers the predicted value of the refrigerant amount index value at the normal operation from the operation data acquired by the operation data acquiring unit 401 in S11, based on the result of learning (the learned model 406) in which the operation data at the normal operation is associated with the refrigerant amount index value.
The order of S12 and S13 may be reversed.
In Step 14 (S14), the determining unit 404 determines the refrigerant amount of the air conditioning system 100 based on a difference or a ratio between the refrigerant amount index value calculated by the calculating unit 402 in S12 and the predicted value of the refrigerant amount index value at the normal operation inferred by the inferring unit 403 in S13. Thereafter, the outputting unit 405 may output the result determined by the determining unit 404.
In Step 21 (S21), the teacher data acquiring unit 501 acquires teacher data (operation data and a refrigerant amount index value at normal operation). The teacher data acquiring unit 501 stores the acquired teacher data in the teacher data storage unit 502.
In step 22 (S22), the learning unit 503 performs machine learning by associating the operation data and the refrigerant amount index value at the normal operation with each other. The learned model is generated as a result of learning by associating the operation data and the refrigerant amount index value at the normal operation with each other.
Hereinafter, various embodiments of the refrigerant amount determination will be described. As described below, the inferring unit 403 may infer information regarding correction of the refrigerant amount index value using at least one of the operation data acquired by the operation data acquiring unit 401 and the refrigerant amount index value calculated by the calculating unit 402, and a learned model (also referred to as a correction model). The determining unit 404 may determine the refrigerant amount of the air conditioning system 100 based on information regarding correction of the refrigerant amount index value.
The data entered into the correction model may be only the refrigerant amount index value calculated from the operation data, or only the operation data. In the correction model, the same data as used to calculate the refrigerant amount index value may be, used, or data different from the data used to calculate the refrigerant amount index value may be used, or data partially same as the data used to calculate the refrigerant amount index value may be used. The data entered into the correction model may be both the refrigerant amount index value and the operation data.
The “information regarding correction of the refrigerant amount index value” output from the correction model may be, for example, as follows: the corrected refrigerant amount index value; corrected range of the refrigerant amount index value; information for specifying the corrected refrigerant amount index value (for example, coefficients a and b of linear correction formula ym(t)=a*y(t)+b when the corrected refrigerant amount index value is ym(t)); and the like.
Specifically, the inferring unit 403 includes a correction unit 403-1 and a past value (buffer function) 403-2. The past value 403-2 stores the past refrigerant amount index value (y(t−1), . . . , y(t−m), . . . ). The past refrigerant amount index value is accompanied by time information (t−1, . . . , t−m, . . . ) including date information (information on the month and day when the refrigerant amount index value was acquired). The correction unit 403-1 acquires the refrigerant amount index value (y(t)) accompanied by the time information (t) from the calculating unit 402, and acquires the past refrigerant amount index value (y(t−1), y(t−2)) from the past value 403-2 using the acquired date information. The correction unit 403-1 sets the present value as Expression 1 described below.
The correction unit 403-1 acquires the data of the same time of the previous year corresponding to (y(t), y(t−1), and y(t−2)) from the past value 403-2, using the date information acquired in the same manner. The correction unit 403-1 defines a variable as Expression 2 described below.
In the expression, y*(t) is the refrigerant amount index value of y(t) at the same time of the previous year. The inner product of Y(t) and Y*(t) defined in this manner may be used as the correction value of the refrigerant amount index value. That is, the inferring unit 403 may output the corrected refrigerant amount index value by inputting y(t) and past values y(t−1), . . . , y(t−m) up to t−m (here, m=2) and past values y*(t−1), . . . , y*(t−m) at the same time of the previous year into the correction model. Using such a corrected refrigerant amount index value facilitates the determination of refrigerant leakage, as illustrated in the graph of
The inferring unit 403 outputs the predicted distribution of the refrigerant amount index value in a normal state or in an abnormal state (leakage state). For example, a predicted distribution obtained by approximating with a normal distribution (predicted statistical parameters: μ0, σ0 (characteristic parameters of the distribution corrected by the correction model)) and an actual distribution (actual statistical parameters: μ, σ) as illustrated in
The inferring unit 403 outputs a predicted cluster of the refrigerant amount index value in a normal state or in an abnormal state (leakage state). For example, a predicted cluster (a cluster corrected by the correction model) and an actual cluster as illustrated in
In <Example 1> and <Example 2>, instead of the corrected range of the refrigerant amount index value, information for specifying the corrected refrigerant amount index value (for example, coefficients a and b of linear correction formula ym(t)=a*y(t)+b when the corrected refrigerant amount index value is ym(t)) may be used.
Correction value of refrigerant amount index value=0
Correction value of refrigerant amount index value=−L1/(L1+L2)
L1: Minimum distance from boundary of the cluster of normal conditions to the point
L2: Minimum distance from boundary of the cluster of abnormal conditions (leakage conditions) to the point
Correction value of refrigerant amount index value=−1
One or more refrigerant amount index values and one or more correction models may be used. For example, the embodiment of
«Data Set for Learning»
The correction model is a model learned by correlating the refrigerant amount index value with operation data at at least one of normal operation and abnormal operation (that is, normal state only, abnormal state only (leakage state), normal state and abnormal state (with distinction), normal state and abnormal state (without distinction)).
The operation data at at least one of normal operation and abnormal operation includes at least one of measured data and pseudo data (that is, only the measured data, only the pseudo data, the measured data and the pseudo data). When the learning data is insufficient, or when the normal data amount and the abnormal data amount are uneven, the accuracy of the correction may be low. Therefore, it is possible to inflate the data amount by creating pseudo normal data and pseudo abnormal data from existing data.
«Output Correction»
The refrigerant amount determining device 400 may further include an output correction unit that corrects the information regarding correction of the refrigerant amount index value.
The output correction unit determines the AI output characteristics such as an initial filled amount (offset amount), a refrigerant leakage rate (the rate of change of AI output) and the like. The calculating unit 402 and the inferring unit 403 (including the correction model 406) are also referred to as artificial intelligence (AI). The output correction unit can reduce erroneous determination by selecting the optimum decision logic according to the characteristics. In addition, the output correction unit determines AI output characteristics such as the initial filled amount (offset amount) and the refrigerant leakage rate (the rate of change of AI output) and changes AI according to the characteristics in order to reduce erroneous determination. For example, when it is determined from the output characteristics of AI-1 that the property is out of gas, the AI can be changed to AI-2 with high accuracy for properties that are running out of gas.
«Input Correction»
The refrigerant amount determining device 400 may further include an input correction unit for correcting the operation data.
The input correction unit may exclude data from AI inputting when the data quality deteriorates, such as short operation time, high start/stop frequency, or small number of indoor units in operation, in order to prevent erroneous determination. In addition, the input correction unit may select the optimum AI according to features such as a small number of data within a certain period of time, a low outdoor temperature, and a small frequency of the compressor.
The input correction unit may create pseudo data of the operation data. The operation data may include at least one of the measured data and the pseudo data.
The refrigerant amount determining device 400 may further include the output correction unit and the input correction unit.
<Determination Result>
For example, the outputting unit 405 may output a numerical value for determining the refrigerant amount (for example, a corrected difference between the refrigerant amount index value and the predicted value=0; SC=0.5) as the determination result. That is, the determining unit 404 determines the current precise trend value in which the variation or noise is removed from the refrigerant amount index value.
For example, the outputting unit 405 may output a category for determining the refrigerant amount (for example, leakage/normal, level A/B/C) or a category for determining the refrigerant amount and its reliability (for example, “leakage; reliability 85%”) as a result of the determination. That is, the determining unit 404 determines whether there is a leakage condition at the present time based on the value obtained by removing variation or noise from the refrigerant amount index value.
<Feedback Of Determination Result>
The determination result may be fed back as follows.
The determination result may be fed back to the determining unit 404. The determining unit 404 may perform the determination using the determination result output by the outputting unit 405. For example, the determining unit 404 may make a first-order determination using its own logic and finally determine by adding the determination result based on past similar conditions referenced from the database. For example, the determining unit 404 may readjust the determination conditions or threshold so as to reduce erroneous determination and improve the correct answer rate based on the determination result within a certain period after detecting the leakage by the default setting (the determining unit 404 may regularly readjust in the same method thereafter). As described above with reference to
The determination result may be fed back to the learned model acquiring unit 407. The learned model acquiring unit 407 may obtain an optimum correction model using the determination result output by the outputting unit 405. For example, the learned model acquiring unit 407 may reacquire the learned model based on the determination result within a certain period after detecting the leakage in the default setting model so that the erroneous determination decreases and the correct answer rate increases.
The determination result may be fed back to the learning unit 503. The learning unit 503 may relearn using the determination result output by the outputting unit 405. For example, the learning unit 503 may create a model that has relearned from the determination result within a certain period after detecting leakage in a default setting model so as to reduce the erroneous determination and improve the correct answer rate.
The determination result may be fed back to the learning dataset. The learning unit 503 may modify the learning data using the determination result output by the outputting unit 405 and relearn the correction model. For example, the learning unit 503 may modify the learning dataset to generate a model relearned from the determination result within a certain period after detecting leakage in a default setting model so as to reduce the erroneous determination and improve the correct answer rate.
«External Data»
In the above, only the operation data is used, but the operation data and external data (for example, external sensor data, image data, and installation status data of the air conditioning system 100) may be used.
For example, the correction model is a model learned by associating the external sensor data, the operation data, and the refrigerant amount index with one another. The operation data acquiring unit 401 further acquires the external sensor data. The inferring unit 403 infers information regarding correction of the refrigerant amount index value using the acquired external sensor data, the operation data, and the correction model. For example, the external sensor data is data of the temperature and pressure sensor (when the sensor that measures the temperature and pressure data is not mounted). For example, the external sensor data is data from a refrigerant gas leakage detection sensor. For example, the external sensor data may be data from a vibration sensor and acceleration pickup.
For example, the correction model is a model learned by associating image data, the operation data, and the refrigerant amount index with one another. The operation data acquiring unit 401 further acquires the image data. The inferring unit 403 infers information regarding correction of the refrigerant amount index value using the acquired image data, the operation data, and the correction model. The image data is image data of the point where a change appears when the refrigerant leaks. For example, the image data is image data of the sight glass installed in the middle of the liquid pipe from the outlet of the condenser to the expansion valve (image data of the generation of bubbles caused by saturation in the pipe due to a low refrigerant amount). For example, the image data is an image taken by injecting a fluorescent agent into a pipe and emitting black light on a part where leakage is likely to occur. For example, the image data is an image of the frost formation on the surface of the outdoor unit heat exchanger fin during heating.
For example, the correction model is a model learned by associating the installation status data of the air conditioning system 100, the operation data, and the refrigerant amount index with one another. The operation data acquiring unit 401 further acquires the installation status data of the air conditioning system 100. The inferring unit 403 infers information regarding correction of the refrigerant amount index value using the acquired installation status data, the operation data, and the correction model. For example, the installation status data of the air conditioning system 100 is the overall length of the pipe, the ratio of the length of the main pipe to the length of the branch pipe, the difference in the installation height between the outdoor unit and the indoor unit, the indoor unit structure (which causes a difference in the indoor unit volume), and the like.
For example, the installation status data of the air conditioning system 100 is filled amount of the refrigerant. By using data on standard refrigerant amount and in-short refrigerant amount when creating a model, it is possible to predict refrigerant amount at the normal operation and at the leakage from the operation data.
While the embodiments have been described, it will be understood that various modifications of embodiments and details are possible without departing from the spirit and scope of the claims.
The present application claims the priority to Japanese Patent Application No. 2019-163572, filed on Sep. 9, 2019, with the Japanese Patent Office, the entire contents of which are hereby incorporated by reference.
Number | Date | Country | Kind |
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2019-163572 | Sep 2019 | JP | national |
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PCT/JP2020/029022 | 7/29/2020 | WO |
Publishing Document | Publishing Date | Country | Kind |
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WO2021/049191 | 3/18/2021 | WO | A |
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