WO2020183539A1 - Système de diagnostic de pannes, procédé de production de règles de prédiction et programme de production de règles de prédiction - Google Patents

Système de diagnostic de pannes, procédé de production de règles de prédiction et programme de production de règles de prédiction Download PDF

Info

Publication number
WO2020183539A1
WO2020183539A1 PCT/JP2019/009448 JP2019009448W WO2020183539A1 WO 2020183539 A1 WO2020183539 A1 WO 2020183539A1 JP 2019009448 W JP2019009448 W JP 2019009448W WO 2020183539 A1 WO2020183539 A1 WO 2020183539A1
Authority
WO
WIPO (PCT)
Prior art keywords
failure
data
prediction
prediction rule
industrial machine
Prior art date
Application number
PCT/JP2019/009448
Other languages
English (en)
Japanese (ja)
Inventor
康司 永松
琢 鴫原
秀秋 田代
林　英松
Original Assignee
三菱電機株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 三菱電機株式会社 filed Critical 三菱電機株式会社
Priority to CN201980093606.5A priority Critical patent/CN113544486B/zh
Priority to PCT/JP2019/009448 priority patent/WO2020183539A1/fr
Priority to JP2019543403A priority patent/JP6647461B1/ja
Publication of WO2020183539A1 publication Critical patent/WO2020183539A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M99/00—Subject matter not provided for in other groups of this subclass

Definitions

  • the present invention relates to a failure diagnosis system for predicting a failure of an industrial machine, a prediction rule generation method, and a prediction rule generation program.
  • a device for diagnosing the state of an industrial machine For example, in Patent Document 1, a neuron set showing the characteristics of the normal state of the industrial machine and a neuron set showing the characteristics of the abnormal state are prepared, and the actual data set obtained in the actual operation of the industrial machine is set to which neuron set.
  • a diagnostic device for diagnosing the state of an industrial machine by determining whether it is similar is disclosed.
  • an unknown abnormal neuron set obtained by compressing the actual data set corresponding to the unknown abnormal state by unsupervised learning of a neural network is applied. It can be newly memorized together with the type of unknown abnormal state.
  • the present invention has been made in view of the above, and an object of the present invention is to provide a failure diagnosis system capable of accurately predicting a sudden failure even when there is little learning data.
  • the failure diagnosis system of the present invention is a failure diagnosis system that predicts a failure of an industrial machine based on a prediction rule for predicting a failure of an industrial machine. It has a rule generator. When a sudden failure occurs in an industrial machine, which is a failure that is not predicted by the prediction rule, the prediction rule generator is based on the adjustment between the parameters of the analysis model in the prediction rule and the parameters of the training data. Generate a new prediction rule to predict.
  • the figure for demonstrating the calculation method of the remaining life time by the failure prediction part which concerns on Embodiment 1. A flowchart showing an example of a prediction rule generation unit according to the first embodiment.
  • FIG. 1 is a diagram showing a configuration example of a failure diagnosis system according to the first embodiment of the present invention.
  • the failure diagnosis system 100 includes a failure prediction device 1 and a plurality of industrial machines 2 1 , 2 2 , ..., 2 n .
  • n is, for example, an integer of 3 or more.
  • Industrial machines 2 1 , 2 2 , ..., 2n are, for example, machines installed in factories to produce products. These industrial machines 2 1 , 2 2 , ..., 2 n are arranged in different factories, for example, but may be arranged in the same factory.
  • each of the industrial machines 2 1 , 2 2 , ..., 2 n is shown without distinction, it may be described as the industrial machine 2.
  • the failure prediction device 1 is connected to a plurality of industrial machines 2 so as to be capable of bidirectional communication via a communication network (not shown).
  • the communication network is, for example, a WAN (Wide Area Network) such as the Internet or a LAN (Local Area Network).
  • the failure prediction device 1 predicts the failure of the industrial machine 2 based on the data transmitted from the industrial machine 2.
  • the failure of the industrial machine 2 includes the failure of the parts or devices constituting the industrial machine 2.
  • FIG. 2 is a diagram showing a configuration example of an industrial machine according to the first embodiment.
  • the industrial machine 2 includes a control unit 60 for controlling the industrial machine 2, a communication unit 61 for transmitting and receiving data between the failure prediction device 1, and a display unit 62 for displaying the data. ..
  • the control unit 60 of the industrial machine 2 can transmit the actual measurement data, the event data, and the operating environment data from the communication unit 61 to the failure prediction device 1. Further, the control unit 60 of the industrial machine 2 can display the data transmitted from the failure prediction device 1 and received by the communication unit 61 on the display unit 62.
  • the measured data is the data acquired from the measuring instrument attached to the industrial machine 2.
  • a measuring instrument measures the state of the equipment or parts constituting the industrial machine 2.
  • the event data is event data indicating an operating state or an abnormal state of the industrial machine 2.
  • the operating environment data is data indicating the operating environment or operating conditions of the industrial machine 2.
  • control unit 60 of the industrial machine 2 can transmit a failure prediction request from the communication unit 61 to the failure prediction device 1 when an abnormal state occurs.
  • the control unit 60 of the industrial machine 2 can display information such as the predicted remaining life information transmitted from the failure prediction device 1 in response to the failure prediction request on the display unit 62 of the industrial machine 2.
  • the failure prediction request can include, for example, information indicating a prediction target portion that is the target of failure prediction.
  • the part to be predicted may be the entire industrial machine 2 or may be a device or a part constituting the industrial machine 2.
  • the control unit 60 of the industrial machine 2 may include a diagnostic unit (not shown) that diagnoses the failure of the industrial machine 2 based on the prediction rule acquired from the failure prediction device 1 via the communication unit 61.
  • the diagnosis unit of the control unit 60 acquires a prediction rule to be described later transmitted from the failure prediction device 1 in response to the failure prediction request, and stores the prediction rule in association with the failure mode corresponding to the above-mentioned abnormal state. be able to.
  • the diagnosis unit of the control unit 60 can diagnose the failure of the industrial machine 2 based on the prediction rule acquired from the failure prediction device 1.
  • the diagnostic unit of the control unit 60 of the industrial machine 2 sends a failure prediction request from the communication unit 61 to the failure prediction device 1 when there is no prediction rule associated with the abnormal state that has occurred. You can also send. Further, the diagnostic unit of the control unit 60 in the industrial machine 2 can periodically transmit a failure prediction request from the communication unit 61 to the failure prediction device 1 regardless of the state of the industrial machine 2.
  • the failure diagnosis system 100 can also include an edge device which is a computer capable of transmitting and receiving data to both the industrial machine 2 and the failure prediction device 1.
  • FIG. 3 is a diagram showing a configuration example of a failure diagnosis system including the edge device according to the first embodiment. As shown in FIG. 3, the edge device 3 is communicably connected to the plurality of industrial machines 2 via the network 4, and is communicably connected to the failure prediction device 1 via the network 5. The edge device 3 can transmit the actual measurement data, the event data, and the operating environment data acquired from the industrial machine 2 via the network 4 to the failure prediction device 1 via the network 5.
  • the edge device 3 transmits / receives data between the communication unit 70 that transmits / receives data to / from a plurality of industrial machines 2 via the network 4 and the failure prediction device 1 via the network 5.
  • a communication unit 71 for processing data and a processing unit 72 for processing data are provided.
  • the processing unit 72 can transmit the measured data, the event data, and the operating environment data received by the communication unit 70 from the plurality of industrial machines 2 from the communication unit 71 to the failure prediction device 1 in a preset data format. ..
  • the data in a unified format can be transmitted to the failure prediction device 1, and the model type or manufacturer of the industrial machine 2 can be transmitted. Can absorb the difference.
  • the edge device may have a configuration having the diagnosis unit and the display unit 62 of the control unit 60 described above.
  • the edge device can display information such as predicted remaining life information transmitted from the failure prediction device 1 in response to a failure prediction request.
  • the edge device can diagnose the failure of the industrial machine 2 based on the prediction rule acquired from the failure prediction device 1. In the following, although it is assumed that the failure prediction request is transmitted from the industrial machine 2, the failure prediction request may be transmitted from the edge device.
  • the failure prediction device 1 includes a master information storage unit 10, a master information registration unit 11, a data reception unit 12, a learning data storage unit 13, a data detection unit 14, a prediction rule generation unit 15, and a prediction rule storage unit. 16, a sudden failure detection unit 17, a failure prediction unit 18, and a data transmission unit 19 are provided.
  • the master information storage unit 10 stores industrial machine information which is information of industrial machine 2 and component component information which is information of equipment or parts constituting the industrial machine 2.
  • the master information storage unit 10 stores industrial machine information for each industrial machine 2, and stores component information for each device or part that constitutes the industrial machine 2.
  • the industrial machine information includes, for example, a machine ID which is unique information of the industrial machine 2, information indicating the type of the industrial machine 2, a part ID which is identification information of a device or a part constituting the industrial machine 2, and authentication information. included.
  • the component information includes the part ID of the device or the part, the information indicating the type of the device or the part, the information indicating the type of the device or the part, and the like.
  • the master information registration unit 11 can add industrial machine information and component information to the master information storage unit 10 and update the industrial machine information stored in the master information storage unit 10.
  • the master information registration unit 11 stores the industrial machine information and component information of the newly connected industrial machine 2 every time the new industrial machine 2 is communicably connected to the failure prediction device 1. It can be stored in 10.
  • the master information registration unit 11 assigns a new machine ID to the industrial machine 2 or assigns a part ID to a device or a part constituting the industrial machine 2. be able to.
  • the data receiving unit 12 receives data from a plurality of industrial machines 2, organizes the received data by machine ID and data type, and stores the received data in the learning data storage unit 13.
  • the data transmitted from the industrial machine 2 is, for example, actual measurement data, event data, operating environment data, and the like.
  • the data receiving unit 12 has an API (Application Programming Interface) open to the industrial machine 2 side, and can receive data transmitted from the industrial machine 2 via the API. Further, the data receiving unit 12 may request the industrial machine 2 to transmit the data, and may receive the data transmitted from the industrial machine 2 based on the request.
  • API Application Programming Interface
  • the data receiving unit 12 can authenticate the industrial machine 2 based on the authentication information stored in the master information storage unit 10.
  • the data receiving unit 12 receives the data from the industrial machine 2 and stores the received data in the learning data storage unit 13 in association with the machine ID or the part ID.
  • the machine ID or the part ID may be included in the data from the industrial machine 2.
  • the learning data storage unit 13 stores the actual measurement data table 20, the event data table 21, and the operating environment data table 22.
  • the data receiving unit 12 adds the received measured data to the measured data table 20 stored in the learning data storage unit 13.
  • the actual measurement data table 20 includes a history of actual measurement data transmitted from each industrial machine 2.
  • FIG. 4 is a diagram showing an example of an actual measurement data table according to the first embodiment.
  • the actual measurement data table 20 includes a plurality of data including "time”, “part ID”, "type”, and “measured value”.
  • the “time” is information indicating the time when the measured data is received by the data receiving unit 12, and in the example shown in FIG. 4, it is the information of the year, month, day, hour, minute, and second.
  • the "part ID” is identification information of a device or a part constituting the industrial machine 2.
  • Type is information indicating the type of actual measurement data including the measured value measured by the measuring instrument.
  • the “type” indicates the type of the measured value measured by the measuring instrument, for example, vibration, temperature, acceleration, rotation speed, operating time, and the like. Vibration is detected, for example, by a vibration sensor installed around the gear. The temperature is detected by a temperature sensor installed around the gear. Acceleration and rotation speed are detected by sensors that detect gear rotation.
  • the "measured value” is a measured value of a sensor installed on a device or part constituting the industrial machine 2. In the following, the measured value may be described as an actually measured value.
  • the data including the time “2018/01/01 10:00:00” includes the part ID "1000", the type “acceleration”, and the measured value "165". Further, the data including the time “2018/01/01 10:00:01” includes the part ID "1000", the type "acceleration", and the measured value "172".
  • FIG. 5 is a diagram showing an example of an event data table according to the first embodiment.
  • the event data table 21 includes a plurality of data including "time”, “machine ID”, “status”, and "code”.
  • the “time” is information indicating the time when the measured data is received by the data receiving unit 12, similarly to the “time” shown in FIG.
  • the “machine ID” is identification information of the industrial machine 2.
  • “Status” is information indicating the status of the industrial machine 2. In the example shown in FIG. 5, the "status” is "up”, “stopped”, or “error”. "Operating” is a state in which the industrial machine 2 is operating. "Stop” is a state in which the industrial machine 2 is stopped. An “error” is a state in which an error has occurred in the industrial machine 2.
  • Code is information indicating an event code, which is an event code.
  • the event code is "1"
  • the event code is "0" and the "status” is “status”. If it is "error”, the event code is "1188”.
  • a different code is assigned to each type of "error” as the event code.
  • an event whose "status” is “error” will be described as a failure event.
  • the data including the time “2018/01/01 9:30” includes the machine ID “0005", the status "operation”, and the code "1".
  • the data including the time “2018/01/01 10:00:04” includes the machine ID "0005", the status "error”, and the code "1188".
  • the operating environment data table 22 includes a history of operating environment data transmitted from each industrial machine 2.
  • FIG. 6 is a diagram showing an example of the operating environment data table according to the first embodiment.
  • the operating environment data table 22 includes a plurality of data including "time”, “machine ID”, “operating conditions”, and “operating environment”.
  • the “time” is information indicating the time when the measured data is received by the data receiving unit 12, similarly to the “time” shown in FIG.
  • the “machine ID” is the identification information of the industrial machine 2.
  • Operating conditions is information indicating the operating conditions of the industrial machine 2.
  • the "operating condition” is the identification information of the material of the product produced by the industrial machine 2, and is, for example, "A05-100-Y" or "A05-100-B".
  • the operating conditions of the industrial machine 2 are not limited to the type of material of the product produced by the industrial machine 2, and may be the type of the product produced by the industrial machine 2, the operation pattern of the industrial machine 2, or the like.
  • the "operating environment” is information indicating the operating environment of the industrial machine 2, and is, for example, the temperature or humidity around the industrial machine 2 or the temperature or humidity inside the industrial machine 2. In the example shown in FIG. 6, the "operating environment” is the ambient temperature of the industrial machine 2.
  • the data including the time “2018/01/01 8:00:00” includes the machine ID "0005", the operating condition "A05-100-Y", and the operating environment "55 degrees”. "Is included. Further, the data including the time “2018/01/01 9:00:00” includes the machine ID "0005", the operating condition "A05-100-Y", and the operating environment "57 degrees”.
  • time is key information used as a synchronization reference between the data in the actual measurement data table 20, the event data table 21, and the operating environment data table 22.
  • the data receiving unit 12 shown in FIG. 1 can receive a failure prediction request from the industrial machine 2 in addition to the above-mentioned actual measurement data, event data, and operating environment data.
  • the data detection unit 14 can transmit an analysis request or the like to the prediction rule generation unit 15, the sudden failure detection unit 17, and the failure prediction unit 18 when the failure prediction request is received by the data reception unit 12. ..
  • the data detection unit 14 requests an analysis of an analysis target part of the industrial machine 2 which is a part to be analyzed, based on, for example, a failure prediction request or event data received from the industrial machine 2 by the data reception unit 12. Can be included in.
  • the failure prediction request indicates a request for immediately executing the failure prediction interpreted from the data received from the industrial machine 2 by the data receiving unit 12.
  • Information that explicitly indicates a failure prediction request may be described as a failure prediction request in the data transmitted from the industrial machine 2.
  • the data receiving unit 12 is provided with an API for requesting failure prediction, and a failure prediction request can be generated by calling the API for requesting such failure prediction from the industrial machine 2.
  • the industrial machine 2 to which the failure prediction request has been transmitted may be described as a prediction target.
  • the data detection unit 14 determines whether or not the failure prediction request has been received from the industrial machine 2 by the data reception unit 12. When the data detection unit 14 determines that the failure prediction request has been received by the data reception unit 12, it transmits an analysis request to the sudden failure detection unit 17 and the failure prediction unit 18. Further, when the data detection unit 14 determines that the failure prediction request has been received by the data receiving unit 12, whether or not the event data from the industrial machine 2 which is the prediction target to which the failure prediction request is transmitted includes the failure event. Is determined. When the data detection unit 14 determines that there is a failure event in the event data from the prediction target object, the data detection unit 14 can request the prediction rule generation unit 15 to generate or update the prediction rule.
  • the data detecting unit 14 acquires the event data of the industrial machine 2 from the event data table 21. Then, the data detection unit 14 determines whether or not the acquired event data includes the target failure event.
  • the target failure event is, for example, a failure event at a time from the time when the failure prediction request is received to a predetermined time before.
  • the data detection unit 14 requests the prediction rule generation unit 15 to generate or update the prediction rule.
  • the data detection unit 14 can detect the risk of sudden failure obtained by monitoring the data stored in the learning data storage unit 13.
  • the risk of sudden failure indicates an increase in sudden failure, which is a failure that is not predicted by the prediction rule, and such a sudden failure does not include a completely random failure.
  • the prediction rule includes an algorithm for predicting a failure of the industrial machine 2.
  • the prediction rule includes an analysis model for predicting a failure of the industrial machine 2 based on the above-mentioned actual measurement data and operating environment data.
  • Such a prediction rule is generated by the prediction rule generation unit 15 using the data stored in the learning data storage unit 13 as learning data.
  • the data detection unit 14 is an analysis model that predicts the measurement value, which is the future actual measurement data, by machine learning using a plurality of actual measurement data included in the actual measurement data table 20 stored in the learning data storage unit 13 as learning data.
  • a measured value prediction model can be generated.
  • the data detection unit 14 can generate a measured value prediction model by performing deep learning using a plurality of actually measured data included in the actually measured data table 20 as learning data.
  • Such a measured value prediction model is, for example, a recurrent neural network that predicts future actual measurement data by using past actual measurement data of a time series as learning data.
  • the data detection unit 14 can obtain the predicted value of the measured data from the measured value prediction model by inputting the measured value included in the measured value received by the data receiving unit 12 into the measured value prediction model.
  • the data detection unit 14 can determine the risk of sudden failure from the size of the difference between the actually measured data newly received by the data reception unit 12 and the predicted value by the measured value prediction model. For example, the data detection unit 14 determines that there is a risk of sudden failure when the difference between the actually measured data newly received by the data reception unit 12 and the predicted value by the measured value prediction model is greater than or equal to a preset value. can do.
  • the risk of sudden failure is, for example, when it is determined that the measured data is data of a new failure mode that is not associated with the training data.
  • the data detection unit 14 can request the prediction rule generation unit 15 to generate or update the prediction rule. Further, when the data detection unit 14 determines that there is a sudden failure risk, the data detection unit 14 can send an analysis request to the sudden failure detection unit 17 and the failure prediction unit 18.
  • the prediction rule generation unit 15 predicts the sudden failure based on the adjustment between the parameters of the analysis model and the parameters of the learning data in the prediction rule. Predictive rules are generated.
  • the prediction rule generation unit 15 includes a generation unit 30 and a reflection unit 31.
  • the generation unit 30 When the data detection unit 14 requests the generation or update of the prediction rule, the generation unit 30 generates or updates the prediction rule that can reproduce the failure event detected by the data detection unit 14.
  • the target failure mode is a failure mode specified by a target failure event detected by the data detection unit 14 when a failure prediction request is received by the data reception unit 12. For example, when the target failure event is the event of the code "1188" shown in FIG. 5, it is the failure mode specified by the code "1188".
  • the generation unit 30 determines that a sudden failure has occurred in the industrial machine 2 which is the prediction target, the generation unit 30 searches for a prediction rule that can reproduce the failure event detected by the data detection unit 14.
  • the search for the prediction rule refers to trying to adjust the parameters of the analysis model and the parameters of the training data.
  • the parameter of the training data is, for example, a correction value of the training data.
  • the parameters of the analysis model are, for example, the type of the approximate expression for calculating the feature amount for predicting the failure of the industrial machine 2 from the learning data, or the coefficient of the approximate expression.
  • the correction value of the learning data is, for example, a value for correcting the unit system of the actual measurement data, the error of the actual measurement data due to the operating environment of the prediction target, the weighting of the learning data, and the like.
  • the generation unit 30 uses the actually measured data stored in the learning data storage unit 13 as learning data, and repeatedly tries the process of adjusting the learning data and the process of adjusting the analysis model.
  • the learning data used in the adjustment process includes actual measurement data up to the occurrence of a sudden failure.
  • the learning data used in the adjustment process includes actual measurement data when a sudden failure occurs in the industrial machine 2 and time-series actual measurement data before the sudden failure occurs in the industrial machine 2.
  • the generation unit 30 identifies the most effective combination of the adjustment contents of the parameters of the analysis model and the adjustment contents of the parameters of the learning data as the cause parameter of the failure by a plurality of trials.
  • the adjustment contents of the learning data are, for example, the correction value of the learning data described above and the type of the measured data used for predicting the failure.
  • the most effective combination is a combination in which the approximate expression of the analysis model reproduces the time-series transition suitable for the prediction of failure from the adjusted learning data.
  • the approximate expression that reproduces the time-series transition suitable for failure prediction refers to an approximate expression that has a strong correlation with the time until the failure.
  • FIG. 7 is a diagram for explaining selection of an approximate expression of the analysis model according to the first embodiment.
  • the feature amount is a value derived by an approximate expression.
  • the approximate expression Gt 2 + 2 At + 1
  • "G" and "A” are actual measurement values of actual measurement data in different measuring instruments.
  • the measured value used in the calculation of the approximate expression is the measured value of the measured data obtained from the measuring instrument itself, or the measured value of the measured data obtained from the measuring instrument is added with a certain value or multiplied by a specific coefficient. It is a value adjusted by such means.
  • the generation unit 30 can determine the failure prediction threshold value that can calculate the failure prediction from the relationship between the feature amount in the range that can be determined to be normal and the feature amount at the time of failure in the approximate expression of the analysis model.
  • the generation unit 30 can also repeatedly try the process of adjusting the training data after first selecting the approximate expression of the analysis model. In this case as well, the generation unit 30 can specify the most effective combination as the cause parameter.
  • the generation unit 30 generates a new prediction rule by adjusting the parameters of the training data in addition to the parameters of the analysis model. Therefore, even when the training data is small, the sudden failure is accurate. It is possible to generate prediction rules that can be predicted well.
  • the generation unit 30 can specify the cause parameter by using the learning data obtained from the prediction target object and the learning data obtained from one or more industrial machines 2 other than the prediction target object as the learning data. ..
  • the generation unit 30 generates a new prediction rule based on the identified cause parameter. Thereby, for example, the prediction accuracy by the prediction rule can be improved, and the prediction rule for predicting the sudden failure can be commonly used in the plurality of industrial machines 2.
  • the generation unit 30 can generate a new prediction rule for predicting a sudden failure based on the adjustment between the parameters of the analysis model and the parameters of the learning data in the prediction rule.
  • the reflection unit 31 associates a new prediction rule with the failure mode and stores it in the prediction rule storage unit 16.
  • the prediction rules generated by the generation unit 30 include analysis model information, measurement data correction value information, analysis model input data type information, clustering information, operating environment data, and failure prediction threshold. And are included.
  • the correction value of the measured data is a value based on the correction value of the learning model included in the cause parameter, and is, for example, the same value as the correction value of the learning model.
  • the clustering information included in the prediction rule indicates the boundary value of the target failure mode.
  • the prediction rule generation unit 15 can determine the clustering information of the target failure mode based on the distribution of the feature amount of each measured data obtained from the industrial machine 2 of the prediction target having the target failure mode.
  • the feature amount is, for example, a value calculated by integrating the difference between a plurality of actually measured data and a predicted value based on the learning data.
  • the prediction rule generation unit 15 can obtain the feature amount of the measured data by the same calculation method as the sudden failure detection unit 17 described later, but it can also be obtained by a calculation method different from the sudden failure detection unit 17. it can.
  • the operating environment data included in the prediction rule is data such as the operating environment or operating conditions of the predicted object when the target failure event occurs.
  • the event data table 21 is in the state shown in FIG. 5
  • the operating environment data table 22 is in the state shown in FIG. 6, and the target failure event is the event of the code “1188” shown in FIG.
  • the time associated with the event of code "1188” is "2018/01/01 10:00:04".
  • the generation unit 30 uses the operating environment data indicating the operating condition "A05-100-B" and the operating environment "49 degrees" associated with the time "2018/01/01 10:00: 00" as the prediction data. Determine as operating environment data to include.
  • the prediction rule includes operating environment data when a failure event occurs. Therefore, the generation unit 30 can generate an analysis model for each operating environment data, that is, a prediction rule including an analysis model according to the operating environment data.
  • the operating environment data may be data that includes at least one of the operating state and the operating environment of the industrial machine 2.
  • the prediction rule storage unit 16 stores the prediction rule information generated or updated by the prediction rule generation unit 15 in association with the failure mode.
  • the sudden failure detection unit 17 can detect a sudden failure for which a prediction rule is not defined.
  • the sudden failure detection unit 17 determines a failure determination for determining a sudden failure of the industrial machine 2 based on the abnormality calculation unit 40 that calculates the abnormality degree of the measured data and the abnormality degree calculated by the abnormality degree calculation unit 40.
  • a unit 41 is provided.
  • the abnormality degree calculation unit 40 acquires the target failure event, which is the failure event of the prediction rule generation target, from the event data table 21, and causes the acquired target failure event to occur.
  • the measured data of all the measurement targets estimated to be measured is extracted from the measured data table 20. That is, the abnormality degree calculation unit 40 extracts data having a causal relationship with the failure to be predicted from the actual measurement data table 20.
  • the data having a causal relationship with the failure of the prediction target is actually measured data showing a characteristic transition toward the time when the industrial machine 2 fails.
  • the data having a causal relationship with the failure to be predicted may be extracted by the data detection unit 14. In this case, the data having a causal relationship with the failure to be predicted is notified from the data detection unit 14 to the abnormality degree calculation unit 40.
  • the anomaly degree calculation unit 40 is an analysis model that predicts future measurement values of each measurement target by machine learning using each measurement data of the period determined to be normal among the extracted measurement data as learning data. Predictive models can be generated. For example, the abnormality degree calculation unit 40 can generate a measurement value prediction model for each measurement target by performing deep learning using the actual measurement data of each measurement target during the period determined to be normal as learning data. Such a measured value prediction model is, for example, a recurrent neural network that predicts future measured values of a measurement target by using past measured data of a time series as learning data. The abnormality degree calculation unit 40 can obtain the predicted value of the measurement target from the actual measurement data of the measurement target received by the data receiving unit 12 by using the measurement value prediction model of each measurement target. The abnormality degree calculation unit 40 may be configured to obtain the predicted value of the measurement target by using the standard deviation of the measured value of the measurement target.
  • the abnormality degree calculation unit 40 calculates the abnormality degree by integrating the difference between the plurality of actually measured data and the predicted value based on the learning data.
  • the failure determination unit 41 determines that a sudden failure has occurred when the degree of abnormality exceeds a preset threshold value.
  • the failure determination unit 41 integrates the abnormalities calculated for each measurement object by the MT method (Maharanobis-Taguchi system) and integrates the abnormality degree. Then, when the integrated abnormality degree exceeds a preset threshold value, it can be determined that a sudden failure has occurred.
  • the failure determination unit 41 determines that a sudden failure has occurred, the failure determination unit 41 transmits the sudden failure information indicating the occurrence of the sudden failure to the industrial machine 2 that has transmitted the failure prediction request via the data transmission unit 19. can do.
  • the industrial machine 2 can receive the sudden failure information transmitted from the failure prediction device 1 via the communication network and display the received sudden failure information on the display unit. As a result, even when a sudden failure is not detected in the industrial machine 2, the manager or the like of the industrial machine 2 can grasp that the sudden failure has occurred.
  • the failure determination unit 41 can transmit the sudden failure information to the prediction rule generation unit 15 via the data detection unit 14.
  • the prediction rule generation unit 15 can determine that the sudden failure has occurred in the industrial machine 2 which is the prediction target, based on the sudden failure information.
  • the failure prediction device 1 may be configured to directly transmit the sudden failure information from the failure determination unit 41 to the prediction rule generation unit 15.
  • the sudden failure detection unit 17 In order to accurately predict a failure even with a small amount of data, the sudden failure detection unit 17 has learned even if the actual measurement data to be predicted is data obtained from an operating environment that does not exist in the operating environment data table 22. Predicted values can be calculated from training data of other operating environments. Then, the sudden failure detection unit 17 calculates the degree of abnormality from the difference in the calculated predicted values, so that, for example, a sudden failure can be detected even at a stage where sufficient learning data in each operating environment is not available for each industrial machine 2. Can be detected.
  • the failure prediction unit 18 When the failure prediction unit 18 receives an analysis request from the data detection unit 14, the failure prediction unit 18 predicts the failure of the analysis target part and sends the information based on the predicted result to the industrial machine 2 that has transmitted the failure prediction request. Can be sent via.
  • the failure prediction unit 18 includes a selection unit 50 for selecting a prediction rule, a filter unit 51 for filtering actual measurement data of a prediction target object, and a prediction unit 52 for calculating the predicted remaining life of the analysis target portion.
  • the selection unit 50 selects the prediction rule associated with the target failure mode from the prediction rule storage unit 16 based on the analysis request from the data detection unit 14. Further, when the prediction rule associated with the target failure mode is not stored in the prediction rule storage unit 16, the selection unit 50 selects a similar prediction rule from the prediction rule storage unit 16. As a result, it is possible to predict a failure even when the learning data is small.
  • the selection unit 50 searches for which failure mode the analysis request from the data detection unit 14 is clustered in when the prediction rule associated with the target failure mode is not stored in the prediction rule storage unit 16. , The prediction rule associated with the failure mode in which the analysis requests are clustered is selected from the prediction rule storage unit 16. In this case, the selection unit 50 determines the clustering information of the target failure mode based on the distribution of the feature amount of each actually measured data of the prediction target object, as in the prediction rule generation unit 15, for example. Then, the selection unit 50 selects a prediction rule having the determined clustering information. As a result, even at the stage where the learning data for classifying the failure mode is small, it is possible to predict the failure of the prediction target object by supplementing with the prediction rule corresponding to the clustering in which the transition of the feature amount is close.
  • the filter unit 51 filters the actually measured data obtained from the industrial machine 2 which is the prediction target, based on the prediction rule selected by the selection unit 50.
  • the filter unit 51 is based on the information on the type of input data included in the prediction rule selected by the selection unit 50, and the information on the type of input data among the measured data obtained from the industrial machine 2 which is the prediction target.
  • the measured data defined in the above is output to the prediction unit 52.
  • the prediction unit 52 inputs the actual measurement data output from the filter unit 51 into the analysis model to obtain the transition of the feature amount of the current actual measurement data at the present time P.
  • FIG. 8 is a diagram for explaining a method of calculating the remaining life time by the failure prediction unit according to the first embodiment. As shown in FIG. 8, the prediction unit 52 uses the feature amount of the current P and the failure prediction threshold value to calculate the difference in the remaining time until the failure determination in the time zone exceeding the failure prediction threshold value, and predicts the analysis target part. Determine the remaining life. In the example shown in FIG. 8, the predicted remaining life is the length from time t11 to time t12.
  • the prediction unit 52 transmits the predicted remaining life information indicating the determined predicted remaining life to the industrial machine 2 via the data transmission unit 19.
  • the industrial machine 2 receives the predicted remaining life information from the failure prediction device 1, the industrial machine 2 uses the predicted remaining life information for, for example, displaying a warning on the operation screen or notifying the operator.
  • the data transmission unit 19 actively transmits data to the industrial machine 2, but it is also possible to transmit the held analysis result at the request of the industrial machine 2.
  • FIG. 9 is a flowchart showing an example of processing of the prediction rule generation unit according to the first embodiment.
  • the prediction rule generation unit 15 adjusts the parameters of the learning data when a sudden failure occurs (step S1). After that, the prediction rule generation unit 15 adjusts the parameters of the analysis model (step S2). Then, the prediction rule generation unit 15 determines whether or not the adjustment end condition is satisfied (step S3).
  • the adjustment end condition is, for example, a condition that the number of repetitions of steps S1 and S2 reaches a preset number, or a condition that an approximate expression that reproduces the time series transition suitable for failure prediction is obtained.
  • step S3: No When the prediction rule generation unit 15 determines that the adjustment end condition is not satisfied (step S3: No), the process returns to step S1. Further, when the prediction rule generation unit 15 determines that the adjustment end condition is satisfied (step S3: Yes), the prediction rule predicts a new prediction rule for predicting a sudden failure based on the processes of steps S1 to S3. It is stored in the storage unit 16 (step S4), and the process shown in FIG. 9 is terminated.
  • the new prediction rule includes, for example, an analysis model for predicting a sudden failure, a correction value of the measurement data input to the analysis model, and information indicating the type of the measurement data input to the analysis model.
  • the above-mentioned failure diagnosis system 100 includes a failure prediction device 1 and a plurality of industrial machines 2, it may be composed of only the failure prediction device 1. Further, the failure prediction device 1 may be composed of one server device or a plurality of server devices.
  • the training data of the prediction rule and the input data to the analysis model are described as the actual measurement data obtained by a measuring instrument such as a sensor, but the training data and the input data are together with the actual measurement data.
  • Data other than the actually measured data may be included, or only data other than the actually measured data may be included.
  • the data other than the actual measurement data is, for example, event data, operating environment data, maintenance management data, or the like.
  • FIG. 10 is a diagram showing an example of the hardware configuration of the failure prediction device 1 according to the first embodiment.
  • the failure prediction device 1 includes a computer including a processor 101, a memory 102, and an interface circuit 103.
  • the processor 101, the memory 102, and the interface circuit 103 can send and receive data to and from each other by the bus 104.
  • the master information storage unit 10, the learning data storage unit 13, and the prediction rule storage unit 16 are realized by the memory 202.
  • the data receiving unit 12 and the data transmitting unit 19 are realized by the interface circuit 103.
  • the processor 101 executes the functions of the data detection unit 14, the prediction rule generation unit 15, the sudden failure detection unit 17, and the failure prediction unit 18 by reading and executing the program stored in the memory 102.
  • the processor 101 is an example of a processing circuit, and includes one or more of a CPU (Central Processing Unit), a DSP (Digital Signal Processor), and a system LSI (Large Scale Integration).
  • the memory 102 is one or more of RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EEPROM (Erasable Programmable Read Only Memory), and EEPROM (registered trademark) (Electrically Erasable Programmable Read Only Memory). Including.
  • the memory 102 also includes a recording medium on which a computer-readable program is recorded. Such recording media include one or more of non-volatile or volatile semiconductor memories, magnetic disks, flexible memories, optical disks, compact disks, and DVDs (Digital Versatile Discs).
  • the failure prediction device 1 may include integrated circuits such as an ASIC (Application Specific Integrated Circuit) and an FPGA (Field Programmable Gate Array).
  • the failure diagnosis system 100 is a failure diagnosis system that predicts the failure of the industrial machine 2 based on the prediction rule for predicting the failure of the industrial machine 2, and is a prediction rule generation unit. 15 is provided.
  • the prediction rule generation unit 15 suddenly occurs based on the adjustment between the parameters of the analysis model and the parameters of the training data in the prediction rule.
  • the prediction rule generation unit 15 adjusts the parameters of the prediction rule after adjusting the parameters of the learning data. Thereby, for example, even if the learning data is small and the learning data is abnormal, the sudden failure can be predicted accurately.
  • the prediction rule generation unit 15 generates a new prediction rule based on learning data obtained from a plurality of industrial machines 2 located at different positions from each other. Thereby, for example, the prediction accuracy by the prediction rule can be improved, and the prediction rule for predicting the sudden failure can be commonly used in the plurality of industrial machines 2.
  • the learning data includes actual measurement data including measurement values measured by a measuring instrument attached to the industrial machine 2.
  • the parameters of the training data are the correction values of the measured data.
  • the prediction rule generation unit 15 adjusts the correction value of the actually measured data when a sudden failure occurs in the industrial machine 2. As a result, for example, even if there is an abnormality in the measured data, it is possible to generate a prediction rule that can accurately predict a sudden failure.
  • the prediction rule generation unit 15 can generate a new prediction rule based on the time-series actual measurement data from before the sudden failure occurs in the industrial machine 2 to the occurrence of the sudden failure. As a result, it is possible to accurately generate a prediction rule having an approximate expression that reproduces a time-series transition suitable for failure prediction.
  • the prediction rule generation unit 15 includes information on an analysis model that predicts a failure of the industrial machine 2 based on actual measurement data, and prediction including operating environment data including at least one of the operating state and the operating environment of the industrial machine 2. Generate rules for each operating environment data. As a result, it is possible to generate a prediction rule including an analysis model according to the operating environment data.
  • the failure prediction unit 18 predicts a sudden failure by using a similar prediction rule when a new prediction rule is not generated by the prediction rule generation unit 15. As a result, it is possible to accurately predict a sudden failure even when the learning data is small.
  • the industrial machine 2 makes a failure prediction request to the failure prediction device 1 via the communication unit 61 for transmitting / receiving data to / from the failure prediction device 1 and when an abnormal state occurs in the industrial machine 2. It includes a control unit 60 for transmission.
  • the prediction rule generation unit 15 generates a new prediction rule when a failure prediction request is transmitted from the industrial machine 2.
  • the industrial machine 2 transmits a failure prediction request to the failure prediction device 1 to deal with the sudden failure.
  • the failure prediction device 1 can generate a prediction rule.
  • the failure diagnosis system 100 includes an edge device 3 that collects data from a plurality of industrial machines 2 and transmits the data format of the collected data to the failure prediction device 1 in a preset format.
  • an edge device 3 that collects data from a plurality of industrial machines 2 and transmits the data format of the collected data to the failure prediction device 1 in a preset format.
  • the configuration shown in the above-described embodiment shows an example of the content of the present invention, can be combined with another known technique, and is one of the configurations without departing from the gist of the present invention. It is also possible to omit or change the part.
  • 1 Failure prediction device 2, 2 1 , 2 2 , ..., 2 n Industrial machinery, 10 Master information storage unit, 11 Master information registration unit, 12 Data reception unit, 13 Learning data storage unit, 14 Data detection unit, 15 Prediction rule generation unit, 16 Prediction rule storage unit, 17 Sudden failure detection unit, 18 Failure prediction unit, 19 Data transmission unit, 20 Actual measurement data table, 21 Event data table, 22 Operating environment data table, 30 Generation unit, 31 Reflection unit, 40 abnormality calculation unit, 41 failure judgment unit, 50 selection unit, 51 filter unit, 52 prediction unit, 100 failure diagnosis system.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

L'invention concerne un système de diagnostic de pannes (100) permettant de prédire les pannes de machines industrielles (21, 22,... , 2n) en fonction de règles de prédiction permettant de prédire des pannes des machines industrielles (21, 22,... , 2n), le système comprenant une unité de production de règles de prédiction (15). Si une panne soudaine, qui est une panne non prise en charge par la prédiction basée sur les règles de prédiction, se produit dans les machines industrielles (21, 22,... , 2n), l'unité de production de règles de prédiction (15) génère, en fonction des ajustements des paramètres de modèle d'analyse et des paramètres de données d'apprentissage des règles de prédiction, une nouvelle règle de prédiction, permettant de prédire la panne soudaine.
PCT/JP2019/009448 2019-03-08 2019-03-08 Système de diagnostic de pannes, procédé de production de règles de prédiction et programme de production de règles de prédiction WO2020183539A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CN201980093606.5A CN113544486B (zh) 2019-03-08 2019-03-08 故障诊断系统、故障预测方法及计算机可读取的记录介质
PCT/JP2019/009448 WO2020183539A1 (fr) 2019-03-08 2019-03-08 Système de diagnostic de pannes, procédé de production de règles de prédiction et programme de production de règles de prédiction
JP2019543403A JP6647461B1 (ja) 2019-03-08 2019-03-08 故障診断システム、故障予測方法、および故障予測プログラム

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2019/009448 WO2020183539A1 (fr) 2019-03-08 2019-03-08 Système de diagnostic de pannes, procédé de production de règles de prédiction et programme de production de règles de prédiction

Publications (1)

Publication Number Publication Date
WO2020183539A1 true WO2020183539A1 (fr) 2020-09-17

Family

ID=69568088

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2019/009448 WO2020183539A1 (fr) 2019-03-08 2019-03-08 Système de diagnostic de pannes, procédé de production de règles de prédiction et programme de production de règles de prédiction

Country Status (3)

Country Link
JP (1) JP6647461B1 (fr)
CN (1) CN113544486B (fr)
WO (1) WO2020183539A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022064769A1 (fr) * 2020-09-24 2022-03-31 国立大学法人大阪大学 Système et procédé de prédiction d'état de dégradation

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115061009B (zh) * 2022-06-01 2024-05-31 国网浙江省电力有限公司嘉兴供电公司 一种输电线故障分析预警方法
CN115981857B (zh) * 2022-12-23 2023-09-19 摩尔线程智能科技(北京)有限责任公司 故障分析系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08202444A (ja) * 1995-01-25 1996-08-09 Hitachi Ltd 機械設備の異常診断方法および装置
JP2007257366A (ja) * 2006-03-23 2007-10-04 Kagawa Univ 診断装置及び診断方法
WO2008114863A1 (fr) * 2007-03-22 2008-09-25 Nec Corporation Dispositif de diagnostic
US20150323425A1 (en) * 2014-05-12 2015-11-12 Samsung Techwin Co., Ltd. System and method for generating facility abnormality prediction model, and computer-readable recording medium storing program for executing the method

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8249830B2 (en) * 2009-06-19 2012-08-21 Xerox Corporation Method and system for automatically diagnosing faults in rendering devices
JP6148316B2 (ja) * 2015-07-31 2017-06-14 ファナック株式会社 故障条件を学習する機械学習方法及び機械学習装置、並びに該機械学習装置を備えた故障予知装置及び故障予知システム
CN106802643A (zh) * 2015-11-26 2017-06-06 通用电气公司 故障预测系统及方法
CN105606353B (zh) * 2016-02-01 2018-03-16 杭州杰牌传动科技有限公司 一种机械故障诊断方法及系统
CN106354118B (zh) * 2016-08-25 2019-08-09 株洲中车时代电气股份有限公司 一种基于故障树的列车故障诊断系统及方法
WO2018140337A1 (fr) * 2017-01-26 2018-08-02 Siemens Aktiengesellschaft Approche semi-supervisée d'unification pour surveillance d'état de machine et diagnostic de défaillance
JP6989398B2 (ja) * 2017-03-24 2022-01-05 株式会社東芝 故障診断装置、故障診断方法、および故障診断プログラム
CN109034368B (zh) * 2018-06-22 2021-10-15 北京航空航天大学 一种基于dnn的复杂设备多重故障诊断方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08202444A (ja) * 1995-01-25 1996-08-09 Hitachi Ltd 機械設備の異常診断方法および装置
JP2007257366A (ja) * 2006-03-23 2007-10-04 Kagawa Univ 診断装置及び診断方法
WO2008114863A1 (fr) * 2007-03-22 2008-09-25 Nec Corporation Dispositif de diagnostic
US20150323425A1 (en) * 2014-05-12 2015-11-12 Samsung Techwin Co., Ltd. System and method for generating facility abnormality prediction model, and computer-readable recording medium storing program for executing the method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022064769A1 (fr) * 2020-09-24 2022-03-31 国立大学法人大阪大学 Système et procédé de prédiction d'état de dégradation

Also Published As

Publication number Publication date
JPWO2020183539A1 (ja) 2021-03-18
JP6647461B1 (ja) 2020-02-14
CN113544486A (zh) 2021-10-22
CN113544486B (zh) 2022-10-28

Similar Documents

Publication Publication Date Title
JP6810097B2 (ja) 異常検出器
JP7282184B2 (ja) 工業プロセスで使用されるコンポーネントから発生する信号の異常を検出及び測定するためのシステムと方法
US6898554B2 (en) Fault detection in a physical system
WO2020183539A1 (fr) Système de diagnostic de pannes, procédé de production de règles de prédiction et programme de production de règles de prédiction
US20170293862A1 (en) Machine learning device and machine learning method for learning fault prediction of main shaft or motor which drives main shaft, and fault prediction device and fault prediction system including machine learning device
JP2018112852A (ja) 故障予知方法、故障予知装置および故障予知プログラム
JP6200833B2 (ja) プラントと制御装置の診断装置
JP6880560B2 (ja) 故障予測装置、故障予測方法及び故障予測プログラム
JPWO2018104985A1 (ja) 異常分析方法、プログラムおよびシステム
WO2018216197A1 (fr) Système de calcul de gravité d'anomalie, dispositif de calcul de gravité d'anomalie et programme de calcul de gravité d'anomalie
CN117270514B (zh) 基于工业物联网的生产过程全流程故障检测方法
WO2013041440A1 (fr) Système et procédé permettant une gestion d'actifs à l'échelle de l'usine
WO2019162648A1 (fr) Diagnostic de système de commande
JP2018173948A (ja) 故障診断装置、故障診断方法、及びコンピュータプログラム
US11726469B2 (en) Error-based method for calculating a remaining useful life of an apparatus
US20130181827A1 (en) Automated incorporation of expert feedback into a monitoring system
US7949497B2 (en) Machine condition monitoring using discontinuity detection
JP6075241B2 (ja) 処置判定装置、処置判定システム、処置判定プログラム及び処置判定方法
WO2020040764A1 (fr) Système et procédé de validation et de correction de données de capteur en temps réel pour une usine en utilisant des modèles basés sur des données existantes de la même usine
CN109313442B (zh) 用于事件检测的自动视觉和声学分析
US20230028038A1 (en) Configuration of asset monitoring systems
Singer et al. A pattern-recognition-based, fault-tolerant monitoring and diagnostic technique
KR20160053977A (ko) 모델 적응을 위한 장치 및 방법
US20200112577A1 (en) Graph-based sensor ranking
EP4254111A1 (fr) Appareil de diagnostic de défaillance, procédé, programme informatique et support d'enregistrement rémanent lisible par ordinateur

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 2019543403

Country of ref document: JP

Kind code of ref document: A

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19918934

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19918934

Country of ref document: EP

Kind code of ref document: A1