TW202125138A - Abnormality diagnosis device and program capable of accurately determining a state of a device to be diagnosed - Google Patents

Abstract

The subject of the present invention is to provide an abnormality diagnosis device capable of accurately determining a state of a device to be diagnosed. The abnormality diagnosis device 100 comprises a failure mode selection part 106 for selecting the failure mode to be the detected target; a teaching data forming part 103 for generating teaching data DT for determining whether the diagnosed target device 10 suffers from a failure based on the data corresponding to the failure mode in the diagnosed target device 10 or other devices, wherein the data is the failure corresponding measurement data; a measurement item importance calculation part 104 for calculating the importance of the measurement item in the failure mode based on the teaching data DT; a feature quantity selection part 105 for selecting a part of the measurement items as the feature quantity for the failure mode based on the calculated importance; an abnormality calculation part 107 for calculating the abnormality degree A corresponding to the failure mode based on the measurement data related to the feature quantity; and a device state determination part 108 for determining the state of the diagnosed target device 10 based on the calculated abnormality degree A. The abnormality diagnosis device further comprises a database for storing measurement data of a plurality of measurement items in the diagnosed target device. The failure corresponding measurement data is data acquired from the diagnosed target device. The teaching data includes abnormal teaching data that is estimated to be abnormal, and normal teaching data that is estimated to be normal. The teaching data forming part selects the failure corresponding measurement data from the failure discovery date to the first predetermined time in the past as the abnormal teaching data, and selects the failure corresponding measurement data from the data immediately before the oldest data among the abnormal teaching data to a second predetermined time in the past as the normal teaching data.

Description

Abnormal diagnosis device and program

The present invention relates to an abnormal diagnosis device and program.

When monitoring the status of windmills and other machinery, in most cases, sensors equipped with the machinery are used to collect physical quantities such as the temperature or acceleration of the machinery at a fixed frequency, and the collected data are analyzed and processed to monitor the machinery’s performance. Status, judge normal/abnormal. In recent years, there has been a proposal for an abnormal diagnosis method that utilizes one of the fields of artificial intelligence technology, namely machine learning technology, learns the operating data of the machine collected in the past (also called "training"), and estimates and judges the current state (the following patent Document 1 and Non-Patent Document 1). For example, in the offer of the following patent document 1, there is a description: "The equipment state monitoring device is equipped with: an abnormality calculation model preparation unit based on the normal time characteristics extracted from the state quantity change data of the monitored equipment when the target equipment is normal The quantity group creates an abnormality calculation model for calculating the abnormality of the monitoring feature group during the monitoring of the monitoring target equipment; the abnormality calculation unit uses the abnormality calculation model to calculate the abnormality of the monitoring feature group ; Anomaly determination unit, which determines whether there is an abnormality in the monitored equipment based on the degree of abnormality; an abnormal contribution degree calculation unit, which calculates a plurality of feature quantities of the monitoring feature quantity group used to calculate the abnormality degree of the abnormality determined by the abnormality determination unit The contribution degree of each individual to the abnormality degree; and the abnormality cause specification part, which specifies the abnormality cause based on the causal matrix that shows the contribution degree and the relationship between the abnormality cause of the monitored device and the plural feature quantities.” [Prior Technical Literature] [Patent Literature]

[Patent Document 1] Japanese Patent Laid-Open No. 2019-128704 [Non-Patent Literature]

[Non-Patent Document 1] Suzuki, Uchiyama, and Yuda: Anomaly detection technology using data exploration. Operations Research, September 2012, pp.506～511

[The problem to be solved by the invention] In the above-mentioned abnormal diagnosis method of machine learning, a part of the measurement data from the monitoring target device is used as the input data. Hereinafter, the measurement data used for the input of the abnormality diagnosis method is referred to as "feature quantity" or "feature quantity group". In devices with complex structures such as wind power generation devices, the types and quantities of measurement data are huge. Therefore, depending on the selection status of the measurement data used as the feature quantity group, the result of the abnormality diagnosis may vary greatly. Previously, there were many cases in which the feature quantity group was selected based on the subjective judgment of the user, and there were cases in which the result of the abnormal diagnosis was incorrect. As mentioned above, Patent Document 1 describes "calculate the abnormality degree of the feature quantity group during monitoring" and "calculate the contribution degree of each of the plurality of feature quantities to the abnormality degree", but in this case, if "used for If the selection of feature group for abnormal diagnosis is inappropriate, there is a problem that the calculated result of "abnormality" or "contribution" is inappropriate. The present invention was completed in view of the above-mentioned matters, and its object is to provide an abnormality diagnosis device and a program that can accurately determine the state of the diagnosis target device. [Technical means to solve the problem]

The abnormality diagnosis device of the present invention for solving the above-mentioned problems is characterized by having: a database that stores measurement data of a plurality of measurement items in the diagnosis target device; a failure mode selection unit that selects the failure mode to be the detection target; The teaching data preparation unit, which generates teaching data for judging whether the diagnosis target device has a failure based on the data corresponding to the failure mode in the diagnosis target device or other devices, that is, the failure correspondence measurement data; the importance of the measurement item A calculation unit that calculates the importance of the measurement item in the failure mode based on the teaching data; a feature quantity selection unit that selects a part of the measurement item as the feature quantity for the failure mode based on the calculated importance An abnormality calculation unit that calculates an abnormality corresponding to the failure mode based on the measurement data related to the characteristic amount; and a device state determination unit that determines the state of the diagnosis target device based on the calculated abnormality. [Effects of Invention]

According to the present invention, the state of the device to be diagnosed can be accurately determined.

[First Embodiment] <Configuration of the first embodiment> Fig. 1 is a block diagram of an abnormality diagnosis system 1 of a preferred first embodiment. In FIG. 1, the abnormality diagnosis system 1 includes a diagnosis target device 10, a sensor unit 20, and an abnormality diagnosis device 100 (computer). In addition, in this embodiment, the diagnostic target device 10 is a wind power generator. The sensor unit 20 includes N (N is plural) sensors 22-1 to 22-N for measuring physical quantities such as temperature, pressure, acceleration, etc. of each part of the diagnostic target device 10. In addition, the sensor unit 20 samples the measurement results of the sensors 22-1 to 22-N, that is, physical quantities, at a specific sampling period, and supplies the results to the abnormality diagnosis device 100 as the measurement data DM. The abnormality diagnosis device 100 diagnoses the state of the diagnosis target device 10 based on the measurement data DM supplied from the sensor unit 20.

The abnormality diagnosis device 100 includes a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), and SSD (Solid State Drive) As the hardware of a general computer, the SSD stores OS (Operating System), applications, various data, etc. The OS and application programs are expanded in RAM and executed by the CPU. In FIG. 1, the inside of the abnormality diagnosis device 100 shows the functions implemented by the application program etc. as blocks.

That is, the abnormality diagnosis device 100 includes: a measurement data acquisition unit 101, a failure data acquisition unit 102, a teaching data preparation unit 103 (teaching data preparation mechanism), and a measurement item importance degree calculation unit 104 (measurement item importance degree calculation mechanism ), feature quantity selection section 105 (feature quantity selection mechanism), failure mode selection section 106 (failure mode selection mechanism), abnormality degree calculation section 107 (abnormality degree calculation mechanism), device state determination section 108 (device state determination mechanism), and Database 110 (database institution).

The measurement data acquisition unit 101 acquires the measurement data DM from the sensor unit 20. The database 110 has one or more memory devices, and stores various data in the form of electronic files. For example, the database 110 stores the data listed below. The content of each material will be described later. ·Measurement data DM acquired by the measurement data acquisition section 101 ·Past fault data DF ·Teaching data DT ·Importance data DQ, which indicates the importance of measurement data, ·The result of abnormal diagnosis is the time series data of abnormal degree A, ·Status data DC, ·Failure mode list ML

Figure 2 is a diagram showing an example of the data structure of the measurement data DM. In the example shown in the figure, the sampling times ts_1, ts_2, ... ts_max are the times of each sampling period. In addition, the measurement data DM includes the measurement results of the respective sensors 22-1 to 22-N (in other words, N measurement items P1 to PN) at the sampling times ts_1, ts_2, ... ts_max. In this way, the measurement data DM is a collection of time series data collected from the sensors 22-1-22-N, which can be expressed as a matrix as shown in FIG. 2.

Returning to FIG. 1, when the fault data acquisition unit 102 finds a fault in the diagnostic target device 10, it records the fault data DF associated with the fault in the database 110. Figure 3 is a diagram showing an example of the fault data DF. As shown in the figure, the fault data DF includes the fault discovery time DFT, the fault mode DFM and the fault corresponding measurement data DFK. Here, the failure discovery date DFT is data indicating the date and time when the user or the failure monitoring device (not shown) discovered the failure of the diagnosis target device 10.

In addition, the failure mode DFM is data corresponding to the specific content of the discovered failure. For example, as shown in the figure, "damage to the pinion of the speed increaser's shaft", "damage to the generator bearing", etc. are mentioned. In addition, the failure-corresponding measurement data DFK is a part of the above-mentioned measurement data DM, and is equivalent to the measurement data DM within a specific period until the DFT when the failure is discovered.

Returning to FIG. 1, the failure mode list ML stored in the database 110 is a list of various failure modes that can be selected as the failure mode DFM (refer to FIG. 3) described above. In addition, the teaching data preparation unit 103 generates the teaching data DT based on the above-mentioned failure data DF.

Fig. 4 is a diagram showing an example of the teaching data DT. In the example shown in the figure, the sampling time ts_a, ts_b, ts_c, ts_d, etc. are the time of each sampling period. However, in FIG. 4, the time progresses from bottom to top, and the sampling time ts_d is equivalent to the DFT when the fault is found (refer to FIG. 3). The sampling time ts_c is a specific time T1 (first specific time) before ts_d, and the sampling time ts_b is the next sampling time before ts_c. In addition, the sampling time ts_a is a specific time T2 (second specific time) before ts_b.

In addition, the teaching data DT includes the measurement results of the sensors 22-1 to 22-N (see FIG. 1) for each of the measurement items P1 to PN at the sampling times ts_a to ts_d. Furthermore, the teaching data DT includes the flag of the abnormal flag DTF. In other words, the teaching data DT includes the fault corresponding measurement data DFK and the abnormal flag DTF contained in the fault data DF (refer to FIG. 3). The abnormality flag DTF is "1" with respect to the sampling times ts_c to ts_d, and is "0" with respect to the sampling times ts_a to ts_b.

Here, the sampling time when the abnormality flag DTF is "1" is the time when the probability of occurrence of abnormality is high, and the sampling time when the abnormality flag DTF is "1" is the time when the probability of occurrence of abnormality is low. In addition, the teaching data DT in the range where the abnormality flag DTF is "1" is called the teaching data DT1 at the time of abnormality, and the teaching data DT in the range of "0" is called the teaching data DT2 in the normal time. Here, the values of the specific times T1 and T2 can be determined based on the part knowledge associated with the corresponding failure mode DFM (refer to FIG. 3), or the operation history of the device 10 to be diagnosed.

Returning to FIG. 1, the measurement item importance calculation unit 104 calculates the importance data DQ based on the above-mentioned teaching data DT. Here, the importance level data DQ includes N importance levels Q1 to QN respectively corresponding to N measurement items (measurement results of the sensors 22-1 to 22-N). There is a correlation between the N measurement items and the value of the abnormal flag DTF, and the importance degrees Q1 to QN represent the magnitude of the influence of each of the N measurement items on the value of the abnormal flag DTF. For example, as the importance levels Q1 to QN, "contribution to the value of the abnormal flag DTF" can be adopted. However, the importance degrees Q1 to QN are not limited to the "contribution degree", and may be other indicators such as "contribution rate" that indicate the magnitude of the influence on the value of the abnormal flag DTF.

The method of calculating the importance Q1～QN can be, for example, for the teaching data DT, using a mechanical learning algorithm such as a decision tree to perform learning on the abnormal teaching data DT1 and the normal teaching data DT2 to obtain the fault corresponding measurement data DFK The importance of each measurement item included is the calculation result. In addition, it is preferable that the importance levels Q1 to QN are normalized in advance so that the total of them becomes a specific value, for example, "1". Figure 5 is a diagram showing an example of importance data DQ. In the example shown in the figure, the measurement items are listed in descending order of importance.

Returning to FIG. 1, the feature selection unit 105 extracts M measurement items (where M<N) from the importance data DQ in descending order of importance, and selects measurement data related to the extracted measurement items As a feature quantity for abnormal diagnosis. The feature quantity M can be set based on the part knowledge associated with the corresponding failure mode DFM (refer to FIG. 3) or the user's designation, but most of them are in the range of, for example, "2" to "10". However, when higher diagnostic accuracy is required, the number of feature quantities M can be further increased. The plural feature quantities selected for abnormality diagnosis are referred to as selected feature quantities DQS (feature quantities, refer to FIG. 5). In the example shown in Figure 5, the number of feature quantities M is set to "3", and the three measurement items of "Wind Speed_Average", "Acceleration Behind Generator Bearing_Max", and "Nacelle Acceleration_Average" are selected. The measurement data is used as the selected characteristic quantity DQS.

In FIG. 1, the failure mode selection unit 106 selects the failure mode DFM to be diagnosed based on the user's operation from the preset failure mode list ML (refer to FIG. 1). In addition, the abnormality degree calculation unit 107 creates a normal model based on the selected feature quantity DQS (refer to FIG. 5) and the normal teaching data DT2 (refer to FIG. 4), and quantifies the distance between the normal model and the actual measured value of the measurement data It is calculated as the degree of abnormality. For example, the abnormality degree calculation unit 107 may use the statistical algorithm of Maharanobis-Taguchi (Maharanobis-Taguchi method) to calculate the abnormality degree A of the diagnostic target device 10 based on the following formula (1). In the following formula (1), x is the M-dimensional feature vector of the selected feature DQS, μ is the average value of the feature vector x in the teaching data DT2 at normal time, and σ is the feature in the teaching data DT2 at normal time The dispersion of the quantity vector x.

The device status determination unit 108 determines whether the status of the diagnostic target device 10 is normal or abnormal with respect to the failure mode DFM based on the value of the abnormality degree A. For example, regarding the failure mode DFM, if the degree of abnormality A is greater than or equal to the specific threshold value A_th, it can be determined as "abnormal", and if the degree of abnormality A does not reach the threshold value A_th, it can be determined as "normal". The device status determination unit 108 stores the result of determining whether the status of the diagnostic target device 10 is normal for the failure mode DFM as status data DC (refer to FIG. 1) in the database 110. That is, the status data DC is data indicating whether the diagnosis target device 10 is normal for each of the failure modes DFM.

Fig. 6 is a diagram showing an example of the time distribution of anomaly degree A. In Fig. 6, the horizontal axis is time, and the vertical axis is abnormality A. In the example shown in the figure, since there is a sequence in which the degree of abnormality A is greater than or equal to the threshold value A_th, the device state determination unit 108 determines that the corresponding failure mode DFM is "diagnosed as an abnormality in the device 10". The processing of the device state determination unit 108 can be represented by a pseudo code as shown in the following equation (2). IF (the timing of abnormality A≧threshold A_th exists?) THEN The device 10 to be diagnosed is abnormal. The ELSE diagnosis target device 10 is normal. …Formula (2)

<Operation of the first embodiment> Next, the operation of the first embodiment will be described. FIG. 7 is a flowchart of an abnormality diagnosis processing program executed in the abnormality diagnosis apparatus 100. Here, the abnormality diagnosis processing program is classified into an offline processing program R10 that is executed before calculating the abnormality degree A, and an online processing program R20 for calculating the abnormality degree A.

In the offline processing program R10, when the processing proceeds to step S12, the teaching data creation unit 103 creates the teaching data DT corresponding to each failure mode DFM (refer to FIG. 3) based on the measurement data DM and the failure data DF. Next, when the process proceeds to step S14, the measurement item importance calculation unit 104 performs decision tree learning on the teaching data DT to create importance data DQ. Next, when the process proceeds to step S16, the feature quantity selection unit 105 selects the selected feature quantity DQS for each failure mode DFM, and stores the selection result in the database 110.

In addition, in the online processing program R20, when the process proceeds to step S22, the failure mode selection unit 106 selects the failure mode DFM to be diagnosed based on the user's operation. Next, when the process proceeds to step S24, the abnormality degree calculation unit 107 calculates the time of abnormality A based on the selected feature quantity DQS (refer to FIG. 5), the normal teaching data DT2 (refer to FIG. 4) and the above formula (1) Sequence distribution, that is, the anomaly degree A at each sampling time. Then, when the process proceeds to step S26, the device state determination unit 108 determines whether the diagnosis target device 10 is abnormal for the corresponding failure mode DFM based on the time series distribution of the abnormality degree A, and updates the state data DC based on the determination result (refer to FIG. 1).

＜Examples of changes＞ The present invention is not limited to the above-mentioned embodiment, and various modifications can be made. The above-mentioned embodiments are exemplified in order to facilitate the understanding of the present invention, and are not necessarily limited to those having all the constitutions described. In addition, other structures may be added to the structure of the above-mentioned embodiment, or a part of the structure may be replaced with another structure. In addition, the control lines or information lines shown in the figure show those deemed necessary for explanation, and may not show all the control lines or information lines that are necessary on the product. It can be considered that almost all the components are actually connected to each other. The possible changes to the above-mentioned embodiment are as follows, for example.

(1) In the above-mentioned embodiment, an example in which a wind turbine generator is applied as the diagnostic target device 10 has been described. However, the diagnostic target device 10 is not limited to a wind power generator, and various devices such as industrial machinery, electric vehicles, railway vehicles, ships, vans, and escalators may be applied as the diagnostic target device 10.

(2) In the above embodiment, in order to calculate the importance Q1 to QN corresponding to the N measurement items, the decision tree learning algorithm is used. However, in order to calculate the importance Q1~QN, it is also possible to apply a machine learning algorithm other than the decision tree. For example, algorithms such as random forest or support vector machine may learn the teaching data DT to obtain the importance levels Q1 to QN.

(3) In the above-mentioned embodiment, in order to perform the abnormality diagnosis of the diagnosis target device 10, the fault data DF obtained from the diagnosis target device 10 itself (refer to FIG. 3) is used. However, for example, there may be cases where there is no fault data DF immediately after the diagnosis target device 10 is installed, or the amount of the fault data DF is too small. Therefore, as the failure data DF, the failure data DF in other devices of the same or similar specifications as the diagnosis target device 10 can also be used, and the teaching data DT and the like can be generated based on the data.

In particular, when the device 10 to be diagnosed is a wind power generation device, the source of the fault data DF, that is, the "other machine" is preferably another machine of the same wind power plant. This is because the wind speed of the device 10 to be diagnosed and the "other machine" , Wind direction, temperature and other natural conditions are similar. In addition, it is also possible to correct the fault data DF or the teaching data DT based on the characteristic difference between the "other equipment" of the inherited source of the fault data DF and the diagnosis target device 10.

(4) In addition, in the above-mentioned embodiment, one abnormality diagnosis device 100 realizes one database 110. However, it is also possible to connect a plurality of abnormal diagnosis devices 100 to a network (not shown), and implement the database 110 in a storage device on the network. In addition, the function of the abnormality diagnosis device 100 can also be realized by performing distributed processing with a plurality of computers on the network.

(5) Since the hardware of the abnormality diagnosis device 100 in the above embodiment can be realized by a general computer, the flowchart shown in FIG. 7 and the programs for executing other various processes mentioned above can be stored in a storage medium, or via a transmission path distribution.

(6) The processing shown in Fig. 7 and the other processings described above have been described as software processing using a program in the above embodiment, but part or all of them can be replaced with ASIC (Application Specific Integrated Circuit): Special application integrated circuit), or FPGA (Field Programmable Gate Array: Field Programmable Gate Array) and other hardware processing.

<Effects of the first embodiment> As described above, the abnormality diagnosis device 100 of this embodiment includes: a failure mode selection unit 106 that selects the failure mode DFM to be the detection target; and a teaching data preparation unit 103 that is based on the diagnosis target device 10 or other devices The data corresponding to the failure mode DFM, namely the failure corresponding measurement data DFK, is created as teaching data DT for determining whether the diagnostic target device 10 has a failure; the measurement item importance calculation unit 104 calculates the failure mode DFM based on the teaching data DT The importance of the measurement items P1 to PN is Q1 to QN; the characteristic quantity selection unit 105, based on the calculated importance Q1 to QN, selects a part of the measurement items P1 to PN as the characteristic quantity (DQS) for the failure mode DFM; The abnormality degree calculation unit 107, which calculates the abnormality degree A corresponding to the failure mode DFM based on the measurement data DM related to the characteristic quantity (DQS); and the device state determination unit 108, which determines the diagnosis target device 10 based on the calculated abnormality degree A The state.

According to the present embodiment, since a part of the measurement items P1 to PN is selected as the characteristic quantity (DQS) for the failure mode DFM based on the calculated importance levels Q1 to QN, the state of the device 10 to be diagnosed can be accurately determined.

In addition, it is preferable that the failure-corresponding measurement data DFK is data acquired from the diagnosis target device 10. This is because it is considered that the failure-corresponding measurement data DFK obtained from the diagnostic target device 10 conforms to the state of the diagnostic target device 10 to a high degree.

In addition, the teaching data DT includes: the teaching data DT1 when the abnormality is presumed to be abnormal, and the teaching data DT2 when the normal is presumed to be normal; the teaching data preparation unit 103 selects the DFT from the time the failure is found to the previous The measurement data DFK corresponding to the fault at the first specific time (T1) is used as the abnormal teaching data DT1, and the data from the earliest (ts_c) data in the abnormal teaching data DT1 is selected from the previous (ts_b) data to the previous 2 The fault corresponding measurement data DFK at a specific time (T2) is used as the normal teaching data DT2. The first and second specific times (T1, T2) are preferably the time set according to the failure mode DFM.

In this way, by setting the first and second specific times (T1, T2) according to the failure mode DFM, it is possible to set appropriate first and second specific times (T1, T2) corresponding to the failure state of the device 10 to be diagnosed.

1: Abnormal diagnosis system 10: Diagnostic target device 20: Sensor section 22-1～22-N: Sensor 100: Abnormal diagnosis device (computer) 101: Measurement data acquisition department 102: Failure data acquisition department 103: Teaching material preparation department (teaching material preparation organization) 104: Measurement item importance calculation unit (measurement item importance calculation mechanism) 105: Feature selection part (feature selection mechanism) 106: Failure mode selection section (failure mode selection mechanism) 107: Abnormal degree calculation unit (abnormal degree calculation mechanism) 108: Device status judging section (device status judging mechanism) 110: database (database institution) A: Abnormality A_th: threshold DC: Status data DF: fault data DFK: Failure corresponding measurement data DFM: failure mode DFT: Time when the fault was discovered DM: Measurement data DQ: Importance data DQS: select feature quantity (feature quantity) DT: Teaching data DT1: Teaching data when abnormal DT2: Normal teaching data ML: List of failure modes P1～PN: Measurement items Q1～QN: Importance R10: Offline processing program R20: Online processing program S12: steps S14: Step S16: steps S22: Step S24: steps S26: Step T1: specific time (1st specific time) T2: specific time (second specific time) ts_1～ts_max: sampling time ts_a～ts_d: sampling time

Fig. 1 is a block diagram of the abnormality diagnosis system of the preferred first embodiment. Figure 2 is a diagram showing an example of the data structure of the measurement data. Figure 3 is a diagram showing an example of fault data. Figure 4 is a diagram showing an example of teaching data. Figure 5 is a diagram showing an example of importance data Figure 6 is a diagram showing an example of the time distribution of abnormality. Fig. 7 is a flowchart of the abnormal diagnosis processing program executed in the abnormal diagnosis device.

1: Abnormal diagnosis system

10: Diagnostic target device

20: Sensor section

22-1~22-N: Sensor

100: Abnormal diagnosis device (computer)

101: Measurement data acquisition department

102: Failure data acquisition department

103: Teaching material preparation department (teaching material preparation organization)

104: Measurement item importance calculation unit (measurement item importance calculation mechanism)

105: Feature selection part (feature selection mechanism)

106: Failure mode selection section (failure mode selection mechanism)

107: Abnormal degree calculation unit (abnormal degree calculation mechanism)

108: Device status judging section (device status judging mechanism)

110: database (database institution)

A: Abnormality

DC: Status data

DF: fault data

DM: Measurement data

DQ: Importance data

DT: Teaching data

ML: List of failure modes

Claims (4)

An abnormality diagnosis device, which is characterized by having: The database, which stores the measurement data of a plurality of measurement items in the diagnosis target device; The failure mode selection part, which selects the failure mode to be the detection object; A teaching data preparation unit, which generates teaching data for judging whether the diagnosis target device has a failure based on the data corresponding to the failure mode in the diagnosis target device or other devices, that is, the failure corresponding measurement data; The measurement item importance calculation unit, which calculates the importance of the measurement item in the above failure mode based on the above teaching data; A feature quantity selection unit, which selects a part of the measurement item as the feature quantity for the failure mode based on the calculated importance degree; An abnormality calculation unit, which calculates the abnormality corresponding to the failure mode based on the measurement data related to the feature quantity; and The device state determination unit determines the state of the diagnosis target device based on the calculated abnormality degree. Such as the abnormal diagnosis device of claim 1, where The above-mentioned fault corresponding measurement data is the data obtained from the above-mentioned diagnosis target device. Such as the abnormal diagnosis device of claim 2, where The above-mentioned teaching data includes: teaching data when it is presumed to be abnormal, and teaching data when it is presumed to be normal; The above-mentioned teaching data preparation department selects the measurement data corresponding to the above-mentioned fault from the time the fault is discovered to the first specific time before, as the above-mentioned abnormal time teaching data, and selects the earliest data from the above-mentioned abnormal time teaching data The above-mentioned fault corresponding measurement data from the previous data to the second specific time before, as the above-mentioned normal teaching data; and The above-mentioned first and second specific times are the times set according to the above-mentioned failure mode. A program used to enable a computer to function as: The database organization, which memorizes the measurement data of multiple measurement items in the diagnosis target device; Failure mode selection mechanism, which selects the failure mode that becomes the detection object; Teaching data preparation mechanism, which is based on the data corresponding to the failure mode in the diagnosis target device or other devices, that is, the failure corresponding measurement data, and prepares the teaching data for judging whether the diagnosis target device has a failure; The measurement item importance calculation mechanism, which calculates the importance of the measurement item in the above failure mode based on the above teaching data; The feature quantity selection mechanism, which selects a part of the above-mentioned measurement items as the feature quantity for the above-mentioned failure mode based on the calculated importance degree; An abnormality calculation mechanism, which calculates the abnormality corresponding to the failure mode based on the above-mentioned measurement data related to the above-mentioned characteristic quantity; and The device state determination mechanism determines the state of the diagnosis target device based on the calculated abnormality degree.

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