WO2017109903A1 - Dispositif et procédé d'estimation de cause de dysfonctionnement - Google Patents

Dispositif et procédé d'estimation de cause de dysfonctionnement Download PDF

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Publication number
WO2017109903A1
WO2017109903A1 PCT/JP2015/086085 JP2015086085W WO2017109903A1 WO 2017109903 A1 WO2017109903 A1 WO 2017109903A1 JP 2015086085 W JP2015086085 W JP 2015086085W WO 2017109903 A1 WO2017109903 A1 WO 2017109903A1
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estimation
cause
model
unit
abnormality
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Japanese (ja)
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康平 丸地
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Toshiba Corp
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Toshiba Corp
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring

Definitions

  • Embodiments of the present invention relate to an apparatus and a method for estimating a cause of an abnormality using data measured by a sensor.
  • Devices and systems that acquire data from sensors and perform desired control perform desired operations while performing self-diagnosis to ensure that they are always operating in the correct state for stable operation.
  • a notification to that effect is sent to inform the operator or user that an abnormality has occurred.
  • the worker or user who received the notification identifies the cause of the abnormal state and takes appropriate measures according to the cause.
  • the abnormal state detected by the system when an error is issued is the abnormal state at the time of detection, and the cause of the abnormality is unknown.
  • the parts operate while affecting each other, and therefore, the part that has issued an abnormality does not necessarily have a cause.
  • the method using the diagnosis rule is a method of making a diagnosis based on rules of expert knowledge and empirical rules such as “cause C when the value of thermometer A is greater than B”.
  • the method using machine learning is a method of making a diagnosis by constructing a diagnosis model obtained by machine learning of past data for which cause investigation has been completed and classifying which case is similar to the past case.
  • the method using a physical or chemical model is a method of simulating system behavior using, for example, a physical law or chemical formula, and detecting a difference between a simulation result and a measured value to identify an abnormality and identify a cause.
  • a common issue for these diagnostic models is to increase the accuracy (accuracy) of the diagnosis.
  • accuracy accuracy
  • a plurality of causes are listed as candidates, and erroneous determination is likely to occur for a specific failure, and it is necessary to construct a model with higher estimation accuracy.
  • the problem to be solved by the present invention is to enable diagnosis with higher accuracy by performing cause estimation by combining cause estimation models.
  • An abnormality cause estimation apparatus is an abnormality cause estimation apparatus that estimates an abnormality cause of the equipment based on sensor data of a sensor installed in the equipment, and includes a data acquisition unit that acquires the sensor data, and the sensor A first storage unit that stores a first estimation model that estimates an abnormality cause of the facility based on data, a first estimation unit that obtains a first estimation cause based on the first estimation model, and A second storage unit that stores a second estimation model that supplements the first estimation cause, and a correspondence table storage unit that stores a correspondence table that associates the first estimation cause with the second estimation model And when the first estimation cause and the second estimation model are associated with each other by the correspondence table, a second estimation unit that obtains a second estimation cause based on the second estimation model; The first probable cause and the second And a display unit that displays the probable cause.
  • the abnormality cause estimation method of the embodiment includes a data acquisition unit that acquires sensor data of a sensor installed in equipment, and a first estimation model that estimates the cause of abnormality of the equipment.
  • a first estimation unit a correspondence table storing a correspondence table associating the first estimated cause with a second estimated model that supplements the first estimated cause, and a second estimated cause based on the second estimated model
  • An abnormality cause estimation method in an abnormality cause estimation device comprising: the sensor data is acquired by the data acquisition unit; and the first estimation unit is based on the sensor data.
  • the first estimation model estimates the first estimation cause, and in the second estimation unit, the first estimation cause and the first estimation based on the correspondence table stored in the correspondence table storage unit Two estimation models If that is correlated, the second estimation model is a method for estimating a second probable cause.
  • FIG. 1 is a block diagram showing an abnormality cause estimation apparatus of the first embodiment
  • FIG. 2 is a flowchart thereof.
  • the abnormality cause estimation apparatus includes a data acquisition unit 20 that acquires sensor data 10 of equipment to be diagnosed, a first estimation unit 30 that performs cause estimation based on a first estimation model 40A, a first A first storage unit 40 that stores the estimation model 40A, a second estimation unit 50 that performs detailed cause estimation based on the second estimation model 60A, a second storage unit 60 that stores the second estimation model 60A, A correspondence table storage unit 70 that stores the correspondence table 70A and a display unit 80 are provided.
  • Sensor data 10 is sensor data measured by a large number of sensors arranged at various locations of the diagnosis target equipment, and is time-series data composed of measured values and measurement times for each sensor.
  • Sensor data 10 may include state variables inside the system.
  • the data acquisition unit 20 includes a communication unit, and acquires a measurement value of a sensor installed in the facility constantly or at a constant timing.
  • the equipment is connected using a USB or a connection port.
  • log data accumulated in a certain amount of memory in the facility may be acquired via a storage medium such as an SD card or a USB memory (S201).
  • the first estimation unit 30 estimates the cause of the abnormality using the first estimation model 40A in the first storage unit 40 with the sensor data 10 of the facility as an input (S202). Specifically, the first estimation unit 30 corresponds to a calculation location for performing cause estimation, the first storage unit 40 corresponds to a storage device such as a hard disk, and the first estimation model 40A performs cause estimation. This corresponds to a program using an algorithm.
  • the cause estimation in the first estimation unit 30 is executed by an arithmetic device such as a CPU (Central Processing Unit).
  • FIG. 3 shows the configuration of the first estimation unit 30 and the first estimation model 40A.
  • the first estimation model 40A used in the present embodiment includes an input feature quantity list 41, an estimation logic 42, and model meta information 43.
  • the first estimating unit includes a feature calculating unit 31, a model executing unit 32, and a result organizing unit 33.
  • the feature calculation unit 31 calculates a feature amount necessary for the estimation logic 42 from the sensor data 10.
  • the feature quantity required by the estimation logic 42 is defined in the input feature quantity list 41.
  • the input feature quantity list 41 has a description expressing characteristics such as an average value of the measurement data of the sensor A in the input feature quantity list 41, and the feature calculation unit 31 interprets it and calculates a desired feature quantity.
  • the input feature quantity list 41 can be omitted by determining the feature quantity to be used in advance.
  • the model execution unit 32 performs cause estimation based on the estimation logic 42 using the feature amount obtained by the feature calculation unit 31.
  • the estimation logic 42 performs cause estimation from the input feature quantity.
  • the cause estimation here is to calculate a value (here called accuracy) that quantifies the possibility of each cause.
  • the result organizing unit 33 finally organizes the accuracy of each cause.
  • FIG. 4 shows an example. When the identification is uniquely specified, the accuracy of the identified cause is 1, and when the accuracy is limited to a plurality, the accuracy of the cause is 1. When the accuracy is originally calculated, the value is used as it is. In addition, all possible causes may be listed in the cause name column, but only main ones may be listed as shown in FIG. 4 and the others may be arranged in other ways.
  • the estimation logic 42 is based on logic by machine learning.
  • the logic by machine learning is a logic constructed using an algorithm that solves a classification problem. Examples of these algorithms include decision trees, random forests, SVM (Support Vector Machine), and neural networks. An algorithm combining these algorithms may be used.
  • the model meta information 43 is meta information related to the model. Similar to the input feature 41, it is not always necessary, but if this information is present, the display unit 80 can provide a user-friendly display. Examples of the model meta information 43 include a model name, a model correct answer rate, a model mixing matrix, and a cause name list to be estimated by the model.
  • the first estimation model 40A is preferably an algorithm that can cover all estimated causes.
  • the second estimation unit 50 determines whether or not the second estimation model 60A corresponding to the cause of the abnormality cause obtained in S202 exists in the second storage unit 60. Is performed by examining the correspondence table 70A in the correspondence table storage unit 70.
  • the second estimation unit 50 corresponds to a calculation location
  • the second storage unit 60 and the correspondence table storage unit 70 correspond to a storage device such as a hard disk
  • the second estimation model is This corresponds to a program using an algorithm for estimating the cause.
  • the cause estimation in the second estimation unit 50 is executed using an arithmetic device such as a CPU.
  • the same storage device may be used as the storage device corresponding to the first storage unit 40, the second storage unit 60, and the correspondence table storage unit 70.
  • the correspondence table 70A and the second estimation model 60A are created in advance from past data and empirical rules. Specifically, when the estimation result from the past data by the first estimation model 40A includes a plurality of estimation causes, or the estimation cause is different from the actual estimation cause (probable estimation cause 2), the second estimation model 60A that supplements the estimation cause is constructed.
  • the correspondence table 70A is a table in which the correspondence between the estimation cause in the first estimation unit 30 and the constructed second estimation model 60A is created as a table. That is, the second estimation model 60A is a model used to increase the accuracy of the estimation cause of the first estimation unit 30.
  • the second estimation model 60A to be selected differs depending on the estimation cause of the first estimation unit 30
  • the second estimation model 60A to be additionally estimated is associated using the correspondence table 70A.
  • the second estimation model 60A can be easily extracted.
  • FIG. 6 is an example of the correspondence table 70A.
  • the estimation cause is associated with the second estimation model 60A. Further, it is possible to associate with the second estimation model 60A using a conditional expression based on the accuracy of the estimation cause.
  • the estimated cause of the first estimating unit 30 is compared with the correspondence table 70A, and if there is a second estimated model corresponding to the estimated cause (in the case of Yes), the process proceeds to S204, and if not, the process proceeds to S205. Proceed to
  • the second estimation unit 50 performs detailed cause estimation using the second estimation model 60A obtained from the correspondence table 70A in the correspondence table storage unit 70.
  • FIG. 7 is a configuration diagram of the second estimation unit 50 and the second estimation model 60A. Comparing FIG. 3 and FIG. 7, a second estimation model selection unit 51 is added to the second estimation unit 50, and the other features calculation unit 52 and model execution unit 53 are the same as those of the first estimation unit 30. And a result organizing unit 54.
  • the second estimation unit 50 is a part that determines the second estimation model 60A used from the correspondence table 70A. If the second estimation model is determined, the remaining operations are estimated in the same manner as the first estimation unit 30 and thus will not be described.
  • the estimated cause of the first estimating unit 30 or the estimated causes of both the first estimating unit 30 and the second estimating unit 50 are displayed on the display unit 80 in S205. indicate. That is, when S203 is No, the display unit 80 displays the estimation cause and the estimation accuracy of the first estimation model so that the user can easily understand. When S203 is Yes, the display unit 80 can display not only the estimated cause of the first estimated model but also the detailed estimated cause based on the second estimated model.
  • the display unit 80 corresponds to a computer monitor, a liquid crystal monitor of a portable terminal, or the like.
  • FIG. 8 shows an example of a screen display in which the estimated causes based on the first estimated model and the estimated causes of the second estimated model are arranged.
  • On the left side of FIG. 8 is an example in which bar graphs are displayed in ascending order of diagnostic accuracy of the first estimation model 40A, and the recall of each cause A and B of the first estimation model 40A is represented by a line graph. .
  • the model meta information 43 has a mixing matrix.
  • the mixing matrix is a table in which the diagnosis results of the diagnosis logic and the actual results are arranged (FIG. 9). In the example of FIG. 9, the number of times that cause A is correctly diagnosed as cause A is 10 times, and the number of times that cause C is mistakenly diagnosed as cause B is one.
  • the recall is an index representing the certainty of the diagnosis result of the diagnosis logic, and is a ratio that the estimated cause is correct with respect to a specific estimated cause.
  • the recall of cause A is 10/17
  • the recall of cause B is 5/9
  • the recall of cause C is 1/3.
  • a more detailed diagnosis result based on the second estimation model is represented by a pie chart, and the model AB, which is the name of the second estimation model, and the diagnostic accuracy rate thereof are displayed.
  • the algorithm related to the estimation logic 42 of the abnormality cause estimation device of the embodiment is based on logic based on machine learning, but other than that, rule-based logic and logic based on a physical or chemical model are also conceivable.
  • FIG. 5 shows an example of logic based on the rule base and an example based on the if-then rule.
  • a threshold value is provided for the feature amount calculated by the feature calculation unit 31 and divided into cases. In this case, if the feature amount A is 90, the feature amount B is 70, and the feature amount C is 100, the cause B or the cause C is estimated, the feature amount A is 90, the feature amount B is 40, and the feature amount C is 100. If there is any, it is estimated as other.
  • These threshold values are determined based on past data and empirical rules.
  • Logic based on a physical or chemical model is a method of estimating the cause of an abnormality by simulating the system behavior to be compared and looking at the difference between the measured value and the simulated value. As described above, in addition to machine learning, various logics can be used to obtain an estimated cause.
  • the first estimation model 40A alone is used for estimation. Makes it possible to estimate the cause with high accuracy.
  • the correspondence table 70A can easily extract the second estimation model 60A that improves the accuracy of the estimation cause in the first estimation unit 30.
  • FIG. 10 is a first modification of the flowchart of the cause estimation process. Since step S1001 is different from FIG. 2, this portion will be described.
  • Process S1001 is a process of confirming with the user whether to perform estimation using the second estimation model when a correspondence table exists. If the user wishes to estimate, the cause is estimated using the second estimation model (S204). When the user does not wish to estimate (for example, when there is no estimation instruction), the estimation cause is displayed without estimating the second estimation model (S205).
  • FIG. 11 shows an example of a screen display when asking the user whether to use the estimated model.
  • the display of the first estimation model is the same as in FIG. At this time, the accuracy and the reproducibility of cause A and cause B are as high as each other.
  • model AB and model AB + are listed as candidates from the correspondence table 70A.
  • Model AB is a model for diagnosing either cause A or cause B
  • model AB + is a model for diagnosing whether cause A and cause B are occurring simultaneously.
  • This information can be obtained by the user by looking at the diagnosis target (FIG. 11).
  • the diagnosis target can be output by registering this information in the model meta information 63.
  • by registering the correct answer rate in the model meta information 63 it can be presented to the user as shown in FIG.
  • the user examines whether to estimate by referring to these pieces of information, and if so, marks the selection checklist and informs the apparatus of the model to be estimated by pressing the estimation execution button based on the selected model.
  • the user can select which estimation model to use when performing the cause estimation in the second estimation unit 50, it is used in detail for diagnosis of the cause of the abnormality. In the case of a person, an estimated cause with higher accuracy can be obtained.
  • FIG. 12 is a second modification of the flowchart of the cause estimation process.
  • the estimation with the first estimation model 40A after the estimation with the first estimation model 40A, the estimation with the second estimation model 60A is performed only once, but in FIG. 12, the second estimation model 60A with respect to the second estimation model 60A is performed. We also estimate.
  • the estimation cause in the second estimation unit 50 is associated with the second estimation model by the correspondence table 70A, and the second estimation model is estimated by the second estimation model. This cause estimation is repeated until the estimation cause in the second estimation unit 50 and the second estimation model are not associated in the correspondence table 70A.
  • the estimation cause and the second estimation model of the second estimation unit 50 are further added. Can be uniquely determined by associating them with the correspondence table 70A.
  • FIG. 13 is a block diagram showing the second embodiment.
  • Implementation procedures of this embodiment include a procedure for performing cause estimation and a procedure for constructing a second estimation model. Since the former is not different from the first embodiment, a procedure for constructing a second estimation model newly increased in the present embodiment will be described.
  • FIG. 14 is a flowchart of this procedure, and the components of FIG. 13 will be described according to the procedure.
  • the first evaluation model 40A is evaluated by the model evaluation unit 90 using the sensor abnormality data in the sensor abnormality data storage unit 100 (S1401).
  • the sensor abnormality data storage unit corresponds to a database storing sensor abnormality data, and is stored in a hard disk, a USB memory, a ROM, or the like. Further, the sensor abnormality data storage unit may be in an external server or the like, and sensor abnormality data may be acquired therefrom.
  • the model evaluation unit 90 is a calculation location and is processed by a CPU or the like.
  • the sensor abnormality data is data in which the cause of the abnormality is added to the sensor data 10 when an abnormality of the facility has occurred in the past.
  • the model evaluation unit 90 uses the data in the sensor abnormality data storage unit 100 to evaluate the first estimation model 40A.
  • the evaluation procedure is the same as that in S202 of FIG. 2, and the diagnosis result and the mixing matrix of each data are calculated as the evaluation result.
  • the diagnosis result is obtained by calculating the accuracy of each cause for each sensor abnormality data, and can be organized as shown in FIG.
  • the mixing matrix is an arrangement of the number of diagnosis results and actual results, and is arranged as shown in FIG.
  • the diagnosis result is calculated based on the accuracy shown in FIG.
  • the cause with the highest accuracy may be selected, or a threshold value may be provided for each cause, and all the causes that are equal to or higher than the threshold value may be selected. You may select using the ratio and difference of a threshold and accuracy.
  • a mixing matrix including a column such as “cause A or cause C” is obtained as shown in FIG. If the cause of abnormality is a combination of a plurality of causes, a mixed matrix including rows such as “cause A and cause C” is obtained as shown in FIG.
  • a second estimation model creation process for multiple causes.
  • S1402 a second estimation model creation process
  • a second estimation model that performs estimation narrowed down from a plurality of candidates is created.
  • FIG. 17 shows the detailed procedure. First, it is confirmed whether the case which estimates multiple causes is high frequency (S1701). This can be confirmed from the mixing matrix.
  • the number of occurrences is the sum of the columns. Whether the number of occurrences is high can be determined by setting a threshold value.
  • a threshold value an absolute number may be set or a ratio of the number of sensor abnormality data may be set.
  • the model construction unit 110 acquires sensor abnormality data of multiple causes from the sensor abnormality data storage unit 100 (S1702). In the case of FIG. 16, the sensor abnormality data of the cause A and the sensor abnormality data of the cause C are acquired from the sensor abnormality data storage unit 100.
  • the model construction unit 110 constructs a model for classifying a plurality of causes using the acquired sensor abnormality data (S1703).
  • a model is constructed as a classification problem of cause A sensor abnormality data and cause C sensor abnormality data.
  • the model is constructed using general machine learning. Major algorithms include decision trees, random forests, SVMs, neural networks, and the like.
  • the second estimation model 60A is constructed for the estimation causes that are difficult to discriminate among the estimation causes in the first estimation model 40A.
  • the configuration may be different from the second estimation model 60A.
  • the model construction unit evaluates the constructed second estimated model (S1704).
  • the evaluation is performed using the sensor abnormality data in the sensor abnormality data storage unit. Since the cause of the abnormality is given to the sensor abnormality data, the accuracy of the model can be understood by evaluating the constructed second estimated model with the sensor abnormality data. The accuracy is calculated mainly using modeling fitting errors and cross validation. At this time, a plurality of classification algorithms can be employed to select a model having the highest evaluation result.
  • the model construction unit 110 is a program that uses an algorithm, and is also a calculation part that evaluates the accuracy of the constructed model. These calculations are performed using a CPU or the like.
  • the second estimation model is to be adopted by judging whether the evaluation result (S1705) is good or bad. Whether it is adopted is determined by the accuracy rate and the threshold value of the F value. Based on the evaluation result of the first estimation model in S1401 of FIG. 14, a threshold value with higher accuracy than the first estimation model is set. If the evaluation result is bad (poor in S1705), the process ends as it is. If the evaluation result is good (good in S1705), the model updating unit 120 updates the second estimated model 60A in the second storage unit 60 and the correspondence table 70A in the correspondence table storage unit 70 (S1706).
  • the actual cause is the sensor abnormality data of the cause A and the sensor abnormality data of the cause C are acquired from the sensor abnormality data storage unit 100.
  • Sensor abnormality data in which cause C occurs at the same time may be acquired, and a model for classifying the three cases in S1703 may be constructed.
  • FIG. 18 shows the detailed procedure. First, it is confirmed whether there are frequent cases of wrong cause estimation (S1801). This can be confirmed from the mixing matrix. In the example of FIG. 19, the cause B is estimated, but the actual cause is A, and there are many erroneous estimates. Whether the number of occurrences is high can be determined by setting a threshold value. As the threshold value, an absolute number may be set or a ratio of the number of sensor abnormality data may be set.
  • the model construction unit 110 acquires sensor abnormality data that is easily mistaken from the sensor abnormality data storage unit 100 (S1802). Data to be acquired is determined according to cases that are easy to make mistakes. In the case of FIG. 19, there are many cases where the case of cause A is mistaken as cause B, but there are few cases where the case of cause B is mistaken as cause A. For this reason, it can be determined that it is difficult to distinguish between cause A and cause B in the case where the first estimation model 40A estimates cause B. Therefore, in S1802, data in which the actual cause is cause A and cause B among the cases estimated by the first estimation model 40A as cause B is acquired, and a model for classifying these is created in S1803.
  • the estimated cause of the first estimated model 40A causes a plurality of estimated causes, or when the estimated cause of the first estimated model 40A is likely to be erroneous.
  • FIG. 22 is a block diagram showing the third embodiment.
  • the implementation procedure includes a procedure for performing cause estimation, a procedure for constructing the second estimation model 60A, and a procedure for constructing a model designated from the outside.
  • the cause estimation procedure is the same as that of the first embodiment and the second estimation model 60A is the same as the second embodiment. Therefore, a newly specified externally specified model is established in the third embodiment.
  • the procedure to do is explained.
  • the procedure is the flowchart of FIG.
  • the external request acquisition unit 130 acquires an external request.
  • the external request is the specification of the model to be created, and is information about the model learning data and the model algorithm.
  • the specifications of the model learning data include the actual cause and the period during which the data was obtained.
  • the model construction unit 110 acquires sensor abnormality data designated from the outside from the sensor abnormality data storage unit 100 (S2302). Subsequently, the model construction unit 110 constructs a second estimated model 60A using the acquired sensor abnormality data and an externally designated algorithm (S2303).
  • the second estimation model 60A is evaluated (S2304), and if the evaluation result is good, the model update unit 120 updates the correspondence table 70A in the second storage unit 60 and the correspondence table storage unit 70. (S2306). Since this procedure is the same as S1704-1706, description thereof is omitted.
  • the second estimation model 60A designated from the outside. If the user is familiar with the diagnosis of the cause of the abnormality and has already narrowed down the cause of the abnormality, the second estimation model 60A can be constructed from the narrowed down cause. Therefore, it is possible to perform estimation intended by the user, leading to improvement in estimation accuracy.
  • FIG. 24 is a block diagram showing the fourth embodiment.
  • the second estimation model 60A designated from the outside can be constructed, whereas in the present embodiment, the first estimation model 40A designated from the outside can be constructed.
  • the external request acquisition unit 130 acquires an external request
  • the model construction unit 110 acquires externally designated sensor abnormality data from the sensor abnormality data storage unit 100 (S2502).
  • the model construction unit 110 constructs the first estimated model 40A using the acquired sensor abnormality data and an externally designated algorithm (S2503).
  • the first estimation model 40A constructed by the model construction unit 110 is evaluated (S2504), and if the evaluation result is good, the model storage unit 120 updates the first storage unit 40 (S2506).
  • the first estimation model 40A designated from the outside.
  • the first estimation model 40A can be constructed.

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Abstract

Un problème abordé par la présente invention est de permettre un diagnostic plus fiable en combinant des modèles d'estimation de cause lors de l'exécution d'une estimation de cause. L'invention concerne un dispositif d'estimation de cause de dysfonctionnement, comprenant : une unité d'acquisition de données qui acquiert des données de capteur d'un capteur qui a été installé dans une installation ; une première unité d'estimation qui, sur la base des données de capteur, estime une cause d'un dysfonctionnement de l'installation à partir d'un premier modèle d'estimation ; et une unité de mémorisation de tables de corrélation qui mémorise des tables de corrélation qui fournissent des corrélations entre des causes estimées et un second modèle d'estimation qui augmente les causes estimées. Le dispositif d'estimation de cause de dysfonctionnement comprend en outre : une seconde unité d'estimation qui, si une association a été établie avec le second modèle d'estimation par le biais des tables de corrélation, estime en plus la cause du dysfonctionnement sur la base du second modèle d'estimation ; et une unité d'affichage qui affiche la cause estimée.
PCT/JP2015/086085 2015-12-24 2015-12-24 Dispositif et procédé d'estimation de cause de dysfonctionnement Ceased WO2017109903A1 (fr)

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JP2019101495A (ja) * 2017-11-28 2019-06-24 横河電機株式会社 診断装置、診断方法、プログラム、および記録媒体
JP2019139375A (ja) * 2018-02-07 2019-08-22 株式会社Ｙｅ Ｄｉｇｉｔａｌ 故障予知方法、故障予知システムおよび故障予知プログラム
JP2019159729A (ja) * 2018-03-12 2019-09-19 株式会社リコー 故障予測システム
JP2020154891A (ja) * 2019-03-20 2020-09-24 株式会社 日立産業制御ソリューションズ 外れ値要因推定支援装置、外れ値要因推定支援方法及び外れ値要因推定支援プログラム
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JP2021087222A (ja) * 2019-11-30 2021-06-03 華為技術有限公司Ｈｕａｗｅｉ Ｔｅｃｈｎｏｌｏｇｉｅｓ Ｃｏ．，Ｌｔｄ． 故障根本原因決定方法及び装置並びにコンピュータ記憶媒体
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JPWO2022091291A1 (fr) * 2020-10-29 2022-05-05
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JP2021087222A (ja) * 2019-11-30 2021-06-03 華為技術有限公司Ｈｕａｗｅｉ Ｔｅｃｈｎｏｌｏｇｉｅｓ Ｃｏ．，Ｌｔｄ． 故障根本原因決定方法及び装置並びにコンピュータ記憶媒体
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WO2021241578A1 (fr) * 2020-05-29 2021-12-02 株式会社ダイセル Dispositif, procédé et programme d'identification de cause de modulation anormale
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