The present invention relates to a machine diagnostic device that diagnoses abnormality of a machine, and a machine diagnostic method.
Machines such as a power generation gas turbine for social infrastructure are demanded to operate for 24 hours a day. It is necessary to prevent unexpected operation stoppage to maintain high operation rate of the machines. To prevent the unexpected operation stoppage, it is necessary to switch a maintenance method from periodic maintenance of the related art based on machine operation time into state monitoring maintenance of appropriately performing preventive maintenance on the basis of a machine state.
To realize the state monitoring maintenance, it is important for a machine diagnostic device to play a role of analyzing sensor data that is collected through various sensors provided to a machine according to an abnormality diagnostic procedure that is determined, and of diagnosing abnormality of the machine and indication of a failure. Here, an abnormality diagnostic procedure represents a processing flow of a computer that processes data acquired from one or more sensors, and diagnoses an indication of abnormality of the machine and the like on the basis of the resultant processing result.
Typically, in a case where the abnormality of the machine is diagnosed in the state monitoring maintenance, an engineer stops the machine in periodic maintenance such as an overhaul. In addition, when the maintenance is performed in a large scale, sensors of the machine are separated once, and component exchange and maintenance of an inner side of the machine are performed. Then, the sensors are returned to the original positions, and the state monitoring maintenance continues. The state monitoring maintenance diagnoses the abnormality of the machine on the basis of a normal state of the machine. Therefore, when it does not return to a sensor state before the maintenance, it is difficult to achieve accurate state monitoring maintenance.
In inventions related to machine sensor attaching method of the related art, consideration is given to an accurate sensor attaching method. For example, as a sensor adjusting method, PTL 1 discloses the following configuration. An operator, who confirms a minimum lift position on a screen of a diagnostic device, compares a sensor output at that time and a standard output value to determine whether or not a deviation exists in an attaching angle (attaching position) (step S5). In a case where the deviation exists, the operator manually adjusts the attaching angle (attaching position) so that the sensor output pertains to a predetermined range (permissible range) including the standard output value (step S6).
PTL 1: JP 2008-196420 A
PTL 1 relates to a technology of comparing an output value of a sensor with a standard output value that is set with design information to determine a sensor position. In a case where this technology is executed after sensor attachment after the state monitoring maintenance, there is no guarantee that the output value of the sensor before the maintenance and the standard output value are in the same level. In addition, typically, a machine that is an object of the state monitoring maintenance has various operation modes. In addition, typically, a sensor value obtained from the sensor, which is provided to the machine, is different for each operation mode. Therefore, it is difficult to reproduce a state of the machine before the maintenance with only the standard output value that is set with the design information.
As described above, in the case of applying the sensor adjusting method of the related art to the state monitoring maintenance, there is a problem that it is difficult to reproduce the state of the machine before the maintenance, and abnormality diagnostic performance deteriorates.
The invention has been made to solve the above-described problem, and an object thereof is to provide a machine diagnostic device that assists with sensor attachment so as to maintain abnormality sensing performance even after maintenance of a machine, and a machine diagnostic method.
In order to achieve the object, a machine diagnostic device according to the present invention includes: a sensor data acquiring unit that acquires time-series sensor data that is measured by a sensor attached to a machine having one or more operation modes; a learning unit that calculates a normal operation model through statistical processing of the sensor data before detachment of the sensor; an abnormality diagnostic unit that diagnoses abnormality of the machine on the basis of the sensor data and the normal operation model; and a sensor adjusting unit that displays an error between a normal operation model before detachment of the sensor and sensor data after attachment of the sensor on a display unit as a sensor adjustment mode when the sensor is attached again to the machine after detachment of the sensor. Another aspect of the invention will be described in embodiments to be described later.
According to the invention, it is possible to assist attachment of a sensor so as to maintain abnormality sensing performance even after maintenance of a machine.
Hereinafter, embodiments of the invention will be described in detail with reference to the accompanying drawings.
The machine diagnostic device 2 collects and accumulates the various pieces of measurement data measured by the various sensors 11 from the machine 1, periodically diagnoses presence or absence of abnormality of the machine 1 in accordance with a predetermined abnormality diagnostic procedure, and notifies the manager 4 of the diagnostic result. When grasping the abnormality of the machine 1 or a cause of the abnormality (contents of a failure) on the basis of the notification of the diagnostic result from the machine diagnostic device 2, the manager 4 gives an instruction for the maintenance person 3 in place to perform a maintenance operation on the machine 1. In addition, when attaching each of the sensors 11 after the maintenance of the machine, the maintenance person attaches the sensor 11 by using information that is displayed on the machine diagnostic device 2.
The sensor data acquiring unit 21 acquires time-series sensor data 31, which is measured, from the sensor 11 attached to the machine 1 having one or more operation modes. The operation mode specifying unit 22 specifies an operation mode from the sensor data 31.
The learning unit 25 calculates a normal operation model 33 through statistical processing of the sensor data 31 before detachment of the sensor 11. The abnormality diagnostic unit 23 diagnoses abnormality of the machine at a predetermined interval by using the sensor data 31 and the normal operation model 33.
As a sensor adjustment mode when attaching the sensor 11 to the machine 1 again after detaching the sensor 11 and performing maintenance of the machine 1, the sensor adjusting unit 24 displays a degree of abnormality (error) between the normal operation model before detachment and the sensor data after sensor attachment on the display unit 42. Furthermore, the degree of abnormality will be described later with reference to
The storage unit 30 stores the sensor data 31 (refer to
Here, for example, the processing unit 20 is an operation processing device such as a microprocessor, and the storage unit 30 is a storage device such as a semiconductor memory and a hard disk drive. The input unit 41 is an input device such as a keyboard and a mouse, and the display unit 42 is a display device such as a liquid crystal device. Each function of the sensor data acquiring unit 21, the operation mode specifying unit 22, the abnormality diagnostic unit 23, the sensor adjusting unit 24, and the learning unit 25 is realized when the operation processing device executes a predetermined program stored in the storage device.
Furthermore, in this embodiment, the machine 1 that is an object to be diagnosed by the machine diagnostic device 2 may be any apparatus as long as the apparatus performs a predetermined function through a mechanical operation. However, in this embodiment, for example, the machine 1 is set as an electric motor, or an apparatus including the electric motor and a mechanical section that is driven by the electric motor for easy comprehension of the contents of the invention. The electric motor is a master component that is mounted in a various production facilities, and converts electric energy into mechanical energy.
One or more sensors 11 are attached to the machine 1 to monitor an operation state. In a case where the machine 1 is an electric motor, for example, a current sensor that measures a current input to the electric motor, one or more vibration sensors which measure vibration of a bearing and the like of the electric motor, a temperature sensor that measures an ambient temperature of the bearing, and the like are attached to the machine 1. The sensors 11 measure the current, the vibration, the temperature, and the like at a time interval that is determined in advance, and supply measured data to the machine diagnostic device 2 as measurement data.
Hereinafter, respective blocks, which constitute the machine diagnostic device 2, will be described in detail with reference to drawings after
The sensor data acquiring unit 21 (refer to
In an example of the sensor data 31 in
In addition, the cycle of measuring the measurement data by each of the sensors 11 may be different from a cycle of transmitting the measurement data from the sensor 11 to the sensor data acquiring unit 21. For example, the sensor 11 may measure the measurement data at an interval of 0.1 seconds, and may collect the measurement data corresponding to one second and supply the measurement data to the sensor data acquiring unit 21 at an interval of one second.
The operation mode data 32 is data for defining the operation mode of the machine 1, and is created by the manager 4 in advance. The operation mode data 32 is used by the operation mode specifying unit 22 to confirm that the measurement data acquired by the sensor data acquiring unit 21 pertains to which operation mode.
In the example in
As illustrated in
Furthermore, the operation mode can be appropriately defined in addition to the operation modes illustrated in
In addition, the operation mode may be defined in combination of various pieces of measurement data. For example, in addition to the current input to the machine 1, a low-temperature starting operation mode that uses an ambient temperature of the machine 1, a booting operation mode at normal temperature, and the like may be defined. However, in measurement data in which variation time of a temperature and the like is very long, it is assumed that the “amplitude” in the operation mode data 32 in
Hereinbefore, the example of the operation modes illustrated in
Next, the operation mode specifying unit 22 converts the time-series data of the measurement data, which is acquired, into time-series data of “amplitude” and “frequency” (step S32). Furthermore, the “amplitude” stated here may be the time-series data itself of the measurement data in a case where a variation period is very greater than an acquisition cycle (sampling cycle) of the measurement data, and in this case, conversion into the “amplitude” is not necessary.
Next, the operation mode specifying unit 22 refers to a plurality of pieces of the operation mode data 32 which are stored in the storage unit 30, and selects one piece of operation mode definition data among the plurality of pieces of operation mode data 32 (step S33).
Next, the operation mode specifying unit 22 compares the time-series data of the “amplitude” and the “frequency” of the measurement data, which is obtained in step S32, with the operation mode definition data that is selected in step S33, more specifically, data (refer to
On the other hand, from the comparison result in step S34, in a case where the two pieces of data do not match each other (No in step S35), the operation mode specifying unit 22 further determines whether or not the operation mode definition data is completely selected in the determination in step S33 (step S37). From the determination result, in a case where the operation mode definition data is not completely selected yet (No in step S37), it returns to step S33, and the processes from step S33 are repetitively executed.
In addition, in a case where it is determined that the operation mode definition data is completely selected in the determination in step S37 (Yes in step S37), the operation mode specifying unit 22 does not specify the operation mode, and terminates the process in
Hereinbefore, the process illustrated with reference to
When receiving the operation mode ID from the operation mode specifying unit 22, the abnormality diagnostic unit 23 (refer to
Here, specifically, the normal operation model 33 is generated in the learning unit 25 through mechanical learning of the sensor data in the machine 1. A normal operation model is information that defines a normal state of the machine for each operation mode. The abnormality diagnostic unit 23 determines abnormality of the machine using a distance with the normal operation model. In addition, the “operation mode ID” is information indicating an operation mode in which the “failure mode” may occur. In addition, the “diagnostic procedure ID” is information that identifies diagnostic procedure information for detection of the “failure mode”.
As can be seem from the example of the normal operation model 33 in
Here, the “diagnostic procedure ID” is information for identification of the diagnostic procedure information 34. In addition, the “sensor” is a name of measurement data that is used in the diagnostic procedure. The example in
The “pre-processing” is information that designates processing performed with respect to measurement data that is designated by the “sensor” during application of the diagnostic algorithm. Examples of the “pre-processing” include filtering processing for removing a noise, movement averaging processing, and the like. In addition, in a case where the measurement data is periodic data, frequency analysis processing and the like can be performed. Furthermore, the example in
The “algorithm” is information that specifies an abnormality detection algorithm that is used in the diagnostic procedure. The example in
The “post-processing” is information that specifies abnormality determination conditions which are used in determination of abnormality of the machine 1 after application of the abnormality detection algorithm, and the like. The example in
Furthermore, in the cluster analysis, n pieces of measurement data, which are designated by the “sensor”, are searched for predetermined time, and thus an n-dimensional vector space, in which the n pieces of measurement data are set as components, is assumed. In addition, cluster information is generated by using measurement data which is previously acquired in the n-dimensional vector space and includes n components in each time. That is, a plurality of the measurement data, which include the n components in each time, are divided into respective clusters in the n-dimensional vector space. In this embodiment, the cluster information (for example, Datafile0) is generated for each operation mode of the machine 1. The cluster information is information that defines a normal state of the machine.
In addition, in a case where measurement data, which does not pertain to any cluster, exists among the plurality of pieces of measurement data measured by the sensor 11, from the measurement data, it is regarded that abnormality occurs, that is, the abnormality or an abnormality symptom is shown in the machine 1.
In the cluster analysis, the “degree of abnormality” is defined as a Euclidean distance between a position at which measurement data at each time is shown and the center of a cluster that is closest to the position in the n-dimensional vector space. In this embodiment, with regard to the degree of abnormality, when the degree of abnormality of 3 or greater, which is obtained through “post-processing” calculation continues for three seconds or longer, it is regarded as the abnormality of the machine 1.
In calculation of the degree of abnormality, first, values of the sensor A, the sensor B, and the sensor C, which are measured in the three-dimensional vector space, are mapped as sensor data 176. Next, a distance between the sensor data 176 and the closest cluster (In
Furthermore, the measurement data stated in this embodiment may be not only actual measurement data obtained by the sensor 11 (refer to
Next, the abnormality diagnostic unit 23 selects one piece of row data in which the operation mode ID (failure mode ID acquired in step S41) is included in the “operation mode ID” column with reference to the normal operation model 33 stored in the storage unit 30 (step S42). In the example of
Next, the abnormality diagnostic unit 23 extracts the diagnostic procedure ID that is included in the normal operation model that is read out (step S43). In the case of data in the first row of the normal operation model 33 in
Next, the abnormality diagnostic unit 23 reads out the diagnostic procedure information 34, which is designated by the diagnostic procedure ID, from the storage unit 30 (step S44), and reads out measurement data, which is a diagnostic object designated in the “sensor” column of the diagnostic procedure information 34, from the storage unit 30 (step S45). In the case of the example of the diagnostic procedure information 34 in
Next, the abnormality diagnostic unit 23 executes “pre-processing”, “algorithm”, and “post-processing”, which are designated by the diagnostic procedure information 34, with respect to the read-out measurement data of a diagnostic object, to perform a diagnostic operation (step S46). For example, in the example of the diagnostic procedure in
Next, the abnormality diagnostic unit 23 determines whether or not the row data, in which the failure mode ID (failure mode ID acquired in step S41) is included, is completely selected from the normal operation model 33 (step S47). Furthermore, the determination is an operation that is performed with respect to the processing result in in step S42. Therefore, in the determination in step S47, when it is determined that the row data, in which the failure mode ID is included, is not completely selected (No in step S47), the abnormality diagnostic unit 23 repeats again the subsequent processes from step S42.
On the other hand, in the determination in step S47, when it is determined that the row data, in which the failure mode ID is included, is completely selected (Yes in step S47), the abnormality diagnostic unit 23 displays a diagnostic result, which is obtained in the diagnostic process in step S46, on the display unit 42 (step S48).
For example, when a command is given from the sensor adjusting unit 24 (specifically, re-learning button 67 in
Next, the learning unit 25 selects one piece of row data in which the operation mode ID (failure mode ID acquired in step S51 is included in the “operation mode ID” column with reference to the normal operation model 33 (step S52). In the example in
Next, the learning unit 25 extracts the diagnostic procedure ID that is included in the normal operation model that is read out (step S53). In the case of data in the first row of the normal operation model 33 in
Next, the learning unit 25 reads out the diagnostic procedure information 34, which is designated by the diagnostic procedure ID, from the diagnostic procedure information 34 (step S54), and reads out measurement data, which is a diagnostic object designated in the “sensor” column of the diagnostic procedure information 34, from the sensor data 31 for a constant period (step S55). In the case of the example of the diagnostic procedure information 34 in
Next, the learning unit 25 executes the learning process (mechanical learning process) by performing “pre-processing” and “algorithm”, which are designated by the diagnostic procedure information 34, with respect to the read-out measurement data of a diagnostic object to calculate a normal operation model (step S56). For example, in the example of the diagnostic procedure in
Next, the learning unit 25 determines whether or not the row data, in which the failure mode ID (the failure mode ID acquired in step S51) is included, is completely selected from the normal operation model 33 (step S57). Furthermore, the determination is a process that is performed with respect to the processing result in in step S52. Therefore, in the determination in step S57, when it is determined that the row data, in which the failure mode ID is included, is not completely selected (No in step S57), the learning unit 25 repeats again the processes from step S52. From the determination in step S57, in a case where the row data, in which the failure mode ID is included, is completely selected (Yes in step S57), the process is terminated.
When an instruction indicating execution of a sensor adjustment mode is input from the input unit 41 after maintenance by the maintenance person 3, the sensor adjusting unit 24 operates. The sensor adjusting process, which is executed by the sensor adjusting unit 24, will be described in detail with reference to
When a command related to a sensor adjustment mode is given from the input unit 41, the sensor adjusting unit 24 gives a command for the abnormality diagnostic unit 23 to perform a diagnostic process by using the sensor data 31, the diagnostic procedure ID of the normal operation model 33, and the diagnostic procedure information 34 corresponding to the diagnostic procedure ID which are stored in the storage unit 30. The sensor adjusting unit 24 receives a result from the abnormality diagnostic unit 23, and gives a display command to the display unit 42. As illustrated in
Next, the sensor adjusting unit 24 selects one piece of row data in which the operation mode ID (failure mode ID acquired in step S111) is included in the “operation mode ID” column with reference to the normal operation model 33 stored in the storage unit 30 (step S112). In the example of
Next, the sensor adjusting unit 24 extracts the diagnostic procedure ID that is included in the normal operation model that is read out (step S113). In the case of data in the first row of the normal operation model 33 in
Next, the sensor adjusting unit 24 supplies (transmits) the diagnostic procedure ID to the abnormality diagnostic unit 23 to give a command related to abnormality diagnosis by using sensor data in acquisition, and obtains an abnormality diagnostic result from the abnormality diagnostic unit 23 (step S114). Next, after receiving the diagnostic result from the abnormality diagnostic unit 23, the sensor adjusting unit 24 gives a command for the display unit 42 to display the result (step S115). Next, the sensor adjusting unit 24 determines whether or not a termination instruction is given from the input unit 41 (step S116). In the determination in step S116, when it is determined that the termination command is not given (No in step S116), the sensor adjusting unit 24 repeats again the processes from step S114. In the determination in step S116, when it is determined that the termination command is given (Yes in step S116), the sensor adjustment mode is terminated. The maintenance person 3 performs sensor attachment while referencing to the diagnostic result displayed on the display unit 42 in step S115.
The maintenance person 3 confirms reproducibility of a sensor state before maintenance while confirming the sensor adjustment screen 60. A threshold value 62 is the upper limit of the degree of abnormality that is set in advance and is required for re-attachment of the sensor 11. Within the threshold value, the reproducibility of the sensor is secured and thus the sensor adjustment operation is terminated. A window 63 represents an operation mode and a sensor name that is an adjustment object. In a case where a plurality of diagnostic procedure IDs exist, a plurality of the graphs 61 are displayed (not illustrated in
A graph 64 and a graph 65 are graphs which simultaneously show time-series data of sensor data of an adjustment object before 30 seconds from current time and time-series data of sensor data of the normal operation model. Each time-series data is periodically updated, and thus it is possible to always confirm a new degree of abnormality. Here, real-time time-series data of “vibration A” and time-series data of the normal operation model in the object sensor are illustrated.
In addition, in a case where pre-processing is included in the diagnostic procedure (frequency analysis in the diagnostic procedure ID “1” in
The maintenance person 3 performs a sensor attaching operation while referencing to the graphs 61, 64, and 66, and the threshold value 62. When determining that the sensor attachment is completed, the maintenance person 3 presses a termination button 68. When the termination button 68 is pressed, the sensor adjustment screen 60 is closed. Furthermore, a re-learning button 67 will be described later with reference to
According to the machine diagnostic device 2 of this embodiment, it is possible to easily assist the maintenance person 3 with sensor attachment adjustment so that abnormality sensing performance can be maintained even after maintenance of the machine 1.
In step S126, the sensor adjusting unit 24 determines whether or not the degree of abnormality (error) becomes equal to or less than a threshold value (first threshold value) that is set in advance. When the degree of abnormality becomes equal to or less than the threshold value (Yes in step S126), the sensor adjusting unit 24 terminates the sensor adjusting process. In a case where the degree of abnormality is greater than the threshold value (No in step S126), the sensor adjusting unit 24 determines that the sensor adjustment is necessary still, and the process returns to step S114.
In Embodiment 2, when the sensor adjusting process is terminated and the sensor adjustment screen 60 is turned off, the maintenance person 3 can immediately grasp a situation in which the sensor adjustment is terminated.
In step S136, the sensor adjusting unit 24 determines whether or not the degree of abnormality (error) becomes equal to or less than a threshold value (first threshold value) that is set in advance. When the degree of abnormality becomes equal to or less than the threshold value (Yes in step S136), with regard to parameters (a correction value and an offset) of an object sensor, the sensor adjusting unit 24 automatically adjusts the parameters so that the degree of abnormality becomes equal to or less than a second threshold value smaller than the first threshold value (step S137), and terminates the process. In a case where the degree of abnormality is greater than the threshold value (No in step S136), the sensor adjusting unit 24 determines that the sensor adjustment is necessary still, and the process returns to step S114.
In Embodiment 3, rough sensor attachment is executed by the maintenance person 3, and minute final adjustment is executed by the sensor adjusting unit 24 in step S137. According to this, it is possible to shorten adjustment time, which is necessary for the maintenance person 3, of the sensor 11.
In step S146, the sensor adjusting unit 24 determines whether or not time, which is set in advance with respect to the degree of abnormality and is equal to or longer than a threshold value (first threshold value), has passed by constant time, or the re-learning button 67 is pressed. When the time, which is equal to or longer than the threshold value set in advance with respect to the degree of abnormality, has passed by constant time, or the re-learning button 67 is pressed (Yes in step S146), the sensor adjusting unit 24 proceeds to step S147. When the time, which is equal to or longer than the threshold value set in advance with respect to the degree of abnormality, has not passed by constant time, or the re-learning button 67 is not pressed (No in step S146), the process returns to step S114.
Next, in step S147, the sensor adjusting unit 24 provides the operation mode ID and the sensor name, which are input to the abnormality diagnostic unit 23 in step S111, to the learning unit 25, and gives a command for the learning unit 25 to learn again the normal operation model. The re-learning command in step S147 is executed when the button 67 (refer to
As described above, according to the machine diagnostic device 2 according to this embodiment, the maintenance person 3 can adjust the sensor attaching operation while recognizing a difference from a normal operation model in the past before maintenance. Accordingly, it is possible to provide a machine diagnostic device and a machine diagnostic method which are applicable to a machine after maintenance.
1 machine
2 machine diagnostic device
3 maintenance person
4 manager
11 sensor
21 sensor data acquiring unit
22 operation mode specifying unit
23 abnormality diagnostic unit
24 sensor adjusting unit
25 learning unit
30 storage unit
31 sensor data
32 operation mode data
33 normal operation model
34 diagnostic procedure information
35 sensor parameter
41 input unit
42 display unit
60 sensor adjustment screen
62 degree of abnormality (error)
Filing Document | Filing Date | Country | Kind |
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PCT/JP2015/057301 | 3/12/2015 | WO | 00 |