Switchgear diagnosis system

Abstract

[Problem] To easily and accurately ascertain an estimation result of an abnormality location and an estimation result of the life of a switchgear in addition to a sign of abnormality of the switchgear. [Solution] In the switchgear diagnosis system according to an embodiment, an abnormality occurrence estimation unit acquires abnormality occurrence estimation data by estimating the presence or absence of abnormality occurrence in the switchgear. An abnormality location estimation unit acquires abnormality location estimation data by estimating an abnormality location where an abnormality has occurred in a plurality of switchgear components. A life estimation unit acquires life data by estimating the life of the switchgear and the lives of the switchgear components. An estimation result integration unit acquires integrated data by integrating the abnormality occurrence estimation data obtained by the abnormality occurrence estimation unit, the abnormality location estimation data obtained by the abnormality location estimation unit, and the life data obtained by the life estimation unit.

Description

Switchgear Diagnostic System

[0001] The embodiment relates to a switchgear diagnostic system.

[0002] Switchgear, such as circuit breakers and disconnectors, are installed in substations and other facilities to open and close electrical circuits. There is a need for a shift in switchgear maintenance from time-based maintenance to condition-based maintenance. In time-based maintenance, maintenance of switchgear is performed according to the elapsed time since the start of operation of the switchgear. In contrast, in condition-based maintenance, maintenance of switchgear is performed according to the condition of the switchgear.

[0003] In order to perform condition-based maintenance, techniques related to diagnostic systems for diagnosing the operating state of switchgears have been proposed.

[0004] Japanese Patent Application Laid-Open No. 2021-149115

[0005] Conventional diagnostic systems detect abnormalities in switchgears and signs of such abnormalities. However, it has been difficult to easily and accurately determine the location of the abnormality in the switchgear where the abnormality occurs and the estimated lifespan of the switchgear.

[0006] Therefore, the problem that the present invention aims to solve is to provide a switching gear diagnostic system that can easily and accurately grasp not only signs of abnormalities in the switching gear, but also estimated results of the abnormal location and estimated results of the switching gear's lifespan.

[0007] A switching gear diagnostic system according to an embodiment includes a storage unit, an abnormality occurrence estimation unit, an abnormality location estimation unit, a life estimation unit, an estimation result integration unit, and an output unit, and diagnoses the operating state of a switching gear configured with a plurality of switching gear components so as to perform switching operations. The storage unit stores measurement data measured regarding the switching operations of the switching gear when the switching gear is performing the switching operations, associating the measurement data with a time axis. The abnormality occurrence estimation unit estimates whether or not an abnormality has occurred in the switching gear through machine learning using, as learning data, normal state data regarding the switching operations obtained in advance and associated with a time axis when the switching gear is in a normal state. This estimates abnormality occurrence estimation data. The abnormality location estimation unit estimates an abnormal location where an abnormality has occurred in the plurality of switching gear components through machine learning using, as learning data, abnormal state data regarding the switching operations obtained in advance and associated with a time axis when the switching gear is in an abnormal state. This estimates abnormality location estimation data. The life estimation unit estimates the life span of the switching gear and the life span of the switching gear components, thereby obtaining life span data. The estimation result integration unit obtains integrated data by integrating the abnormality occurrence estimation data obtained by the abnormality occurrence estimation unit, the abnormality location estimation data obtained by the abnormality location estimation unit, and the lifetime data obtained by the lifetime estimation unit. The output unit outputs the integrated data obtained by the estimation result integration unit. The abnormality occurrence estimation unit learns a normal waveform pattern when the switching device is in a normal state from the plurality of normal state data, obtains a feature amount by determining the degree of deviation between the normal waveform pattern and each of the plurality of normal state data, obtains a feature amount normal range, obtains a first measurement data feature amount by determining the degree of deviation between the measurement data stored in the storage unit and the normal waveform pattern, and performs an estimation of whether an abnormality has occurred in the switching device based on the position of the first measurement data feature amount and the feature amount normal range.The abnormality location estimation unit learns, from a plurality of abnormal state data, an abnormal waveform pattern when each of a plurality of switching device components is in an abnormal state, calculates a feature abnormality range by calculating a degree of deviation between the abnormal waveform pattern and each of the plurality of abnormal state data as a feature, calculates a second measurement data feature by calculating a degree of deviation between the measurement data stored in the storage unit and the abnormal waveform pattern, and estimates an abnormal location where an abnormality has occurred in the plurality of switching device components based on the position of the second measurement data feature and the feature abnormality range. The life estimation unit has a normality score calculation unit, an abnormality location score calculation unit, and a life calculation unit. When the first measurement data feature is located within the feature normal range, the normality score calculation unit calculates, as normality score data, a degree of deviation of the first measurement data feature from a boundary of the feature normal range. The abnormality location score calculation unit calculates, as abnormality location score data, a degree of deviation of the second measurement data feature value from a boundary of the feature value abnormality range when the second measurement data feature value is located outside the feature value abnormality range. The lifespan calculation unit calculates switching device life estimation data as lifespan data by estimating a lifespan of the switching device based on a transition of the normality score data calculated by the normality score calculation unit, and calculates switching device component life estimation data as lifespan data by estimating lifespans of the multiple switching device components based on the transition of the abnormality location score data calculated by the abnormality location score calculation unit.

[0008] FIG. 1A is a diagram schematically illustrating an example of a switchinggear 1 to be diagnosed by the switchinggear diagnostic system according to the embodiment. FIG. 1B is a diagram schematically illustrating an example of a switchinggear 1 to be diagnosed by the switchinggear diagnostic system according to the embodiment. FIG. 2 is a block diagram schematically illustrating a configuration of a switchinggear diagnostic system 4 according to the embodiment. FIG. 3A is a functional block diagram illustrating main functions of the switchinggear diagnostic system 4 according to the embodiment. FIG. 3B is a block diagram schematically illustrating functions of a life estimation unit 50 in the switchinggear diagnostic system 4 according to the embodiment. FIG. 4 is a flow chart illustrating the operation of the switchinggear diagnostic system 4 according to the embodiment. FIG. 5A is a diagram illustrating an example of measurement data D3 input to an input unit 41 in the switchinggear diagnostic system 4 according to the embodiment. FIG. 5B is a diagram illustrating an example of measurement data D42 stored in a storage unit 42 in the switchinggear diagnostic system 4 according to the embodiment. FIG. 6A is a diagram schematically illustrating an example of normal state data D61 in the switchinggear diagnostic system 4 according to the embodiment. FIG. 6B is a diagram schematically illustrating an example of a normal waveform pattern PS in the switchinggear diagnostic system 4 according to the embodiment. FIG. 6C is a diagram schematically illustrating an example of how the degree of deviation between the normal waveform pattern PS and the normal state data D61 is calculated in the switchinggear diagnostic system 4 according to the embodiment. FIG. 6D is a diagram schematically illustrating an example of how the normal feature range TS is calculated in the switchinggear diagnostic system 4 according to the embodiment. FIG. 7A is a diagram schematically illustrating an example of how the abnormality occurrence estimation unit 43 estimates the presence or absence of an abnormality based on the normal feature range TS in the switchinggear diagnostic system 4 according to the embodiment. FIG. 7B is a diagram schematically illustrating an example of how the abnormality occurrence estimation unit 43 estimates the presence or absence of an abnormality based on the normal feature range TS in the switchinggear diagnostic system 4 according to the embodiment. FIG. 8A is a diagram schematically illustrating an example of abnormal state data D62 in the switchinggear diagnostic system 4 according to the embodiment. FIG. 8B is a diagram schematically illustrating an example of an abnormal waveform pattern PD in the switchinggear diagnostic system 4 according to the embodiment. FIG. 8C is a diagram schematically illustrating an example of how the degree of deviation between the abnormal waveform pattern PD and the abnormal state data D62 is calculated in the switching device diagnostic system 4 according to the embodiment.FIG. 8D is a diagram schematically illustrating an example of how the feature quantity abnormality range TD is calculated in the switchinggear diagnostic system 4 according to the embodiment. FIG. 9A is a diagram schematically illustrating an example of how the abnormality location estimation unit 44 estimates an abnormality location based on the feature quantity abnormality range TD in the switchinggear diagnostic system 4 according to the embodiment. FIG. 9B is a diagram schematically illustrating an example of how the abnormality location estimation unit 44 estimates an abnormality location based on the feature quantity abnormality range TD in the switchinggear diagnostic system 4 according to the embodiment. FIG. 10A is a diagram schematically illustrating an example of how the normality score calculation unit 510 calculates normality score data SS in the switchinggear diagnostic system 4 according to the embodiment. FIG. 10B is a diagram schematically illustrating an example of how the abnormality location score calculation unit 520 calculates abnormality location score data SD in the switchinggear diagnostic system 4 according to the embodiment. FIG. 11A is a diagram schematically illustrating an example of how the life calculation unit 530 calculates switchinggear life estimation data D530a in the switchinggear diagnostic system 4 according to the embodiment. Fig. 11B is a diagram schematically showing an example of how the life calculation unit 530 calculates the switching gear component life estimation data D530b in the switching gear diagnostic system 4 according to the embodiment. Fig. 12 is a diagram schematically showing an example of an image related to the integrated data D45 output by the output unit 46 in the switching gear diagnostic system 4 according to the embodiment. Fig. 13 is a diagram schematically showing an example of an image related to the integrated data D45 output by the output unit 46 in the switching gear diagnostic system 4 according to the first modification.

[0009] [A] Switchgear 1 Before describing the switchgear diagnostic system of the embodiment, the switchgear 1 to be diagnosed by the switchgear diagnostic system will be described.

[0010] 1A and 1B are diagrams schematically illustrating an example of a switching device 1 to be diagnosed by a switching device diagnostic system according to an embodiment. Fig. 1A illustrates the case where the switching device is in an open state (shut-off state), and Fig. 1B illustrates the case where the switching device is in a closed state (closed state).

[0011] The switchgear 1 is, for example, a gas circuit breaker, and is configured with a plurality of switchgear components to perform opening and closing operations as shown in Figures 1A and 1B. Here, the switchgear 1 includes a switchgear main body 11 and an operation mechanism 12, and is configured to open or close an electric circuit by performing opening and closing operations.

[0012] In the switchgear 1, the switchgear body 11 includes a pressure vessel 20, a movable contact 21, and a fixed contact 22. The pressure vessel 20 is filled with, for example, insulating gas. The insulating gas is, for example, SF ６ The movable contact portion 21 and the fixed contact portion 22 are arranged coaxially. The movable contact portion 21 is supported by the pressure vessel 20 so as to be movable along the axial direction. The fixed contact portion 22 is fixed to the pressure vessel 20.

[0013] In the switching device 1, the operating mechanism 12 is configured to move the movable contact 21 along the axial direction. When switching to an open state, the operating mechanism 12 separates the movable contact 21 from the fixed contact 22 (see FIG. 1A). When switching to a closed state, the operating mechanism 12 brings the movable contact 21 into contact with the fixed contact 22 (see FIG. 1B). The operating mechanism 12 is, for example, a spring operating mechanism and is configured with a plurality of switching device components (springs, links, etc.).

[0014] [B] Overview of Switchgear Diagnostic System 4 The configuration of the switchgear diagnostic system 4 used to diagnose the operating state of the above-described switchgear 1 (see FIGS. 1A and 1B) will be described.

[0015] FIG. 2 is a block diagram that schematically shows the configuration of a switching device diagnostic system 4 according to an embodiment.

[0016] The switching device diagnostic system 4 receives as input the measurement data D3 obtained by the sensor 3 detecting the switching device 1 while the switching device 1 is performing an opening and closing operation.

[0017] The sensor 3 is, for example, a displacement sensor attached to the operation mechanism unit 12, and detects information regarding the displacement X (see FIG. 1B ) of the movable contact unit 21 relative to the fixed contact unit 22 as measurement data D3, and the measurement data D3 is input to the switching device diagnostic system 4.

[0018] The switchgear diagnostic system 4 is configured to diagnose the operating state of the switchgear 1 based on the input measurement data D3. Here, the switchgear diagnostic system 4 diagnoses, for example, the operating state of the operation mechanism unit 12 that constitutes the switchgear 1. It is preferable that the switchgear diagnostic system 4 is provided with a noise shield (not shown) inside to prevent it from receiving electromagnetic noise from noise sources including the switchgear 1.

[0019] [C] Configuration of Switchgear Diagnostic System 4 FIG. 3A is a functional block diagram showing the main functions of the switching gear diagnostic system 4 according to the embodiment.

[0020] 3A , the switching gear diagnostic system 4 includes an input unit 41, a storage unit 42, an abnormality occurrence estimation unit 43, an abnormality location estimation unit 44, a life estimation unit 50, an estimation result integration unit 45, and an output unit 46. The switching gear diagnostic system 4 is configured, for example, by a computer, and is configured to function as each unit by the computer executing a program.

[0021] Each component of the switching device diagnostic system 4 will be described in turn.

[0022] [C-1] Input Unit 41 The input unit 41 includes, for example, an input interface, and receives measurement data D3 (time-series data) measured regarding the opening and closing operation of the opening and closing device 1 from the sensor 3. The input unit 41 constantly receives the measurement data D3.

[0023] [C-2] Storage Unit 42 The storage unit 42 includes, for example, a storage device such as a hard disk drive and memory, and stores the measurement data D3 output from the input unit 41 in association with a time axis.

[0024] [C-3] Abnormality Occurrence Estimation Unit 43 The abnormality occurrence estimation unit 43 receives the measurement data D42 stored in the memory unit 42 in association with a time axis. The abnormality occurrence estimation unit 43 then obtains abnormality occurrence estimation data D43 by estimating the presence or absence of an abnormality in the switching device 1 based on the measurement data D42. The abnormality occurrence estimation unit 43 is configured, for example, by a calculation device including a central processing unit (CPU), and executes estimation of abnormality occurrence using a program. Here, the abnormality occurrence estimation unit 43 is, for example, a one-class classifier.

[0025] In this embodiment, after the opening and closing device 1 completes the opening and closing operation, the abnormality occurrence estimation unit 43 estimates whether or not an abnormality has occurred.

[0026] Although details will be described later, the abnormality occurrence estimation unit 43 estimates the presence or absence of an abnormality by machine learning using normal state data D61 obtained in advance in association with a time axis regarding the switching operation when the switching device 1 is in a normal state as learning data. In the present embodiment, the normal state data D61 is information obtained using a physical simulator 5 of the switching device 1. The physical simulator 5 is a mechanical analysis model that can reproduce the operation of the switching device 1 and is capable of simulating the operation of the switching device 1. In the present embodiment, information corresponding to a displacement X (see FIG. 1B ) by which the movable contact unit 21 moves relative to the fixed contact unit 22 when the switching device 1 is in a normal state is obtained as the normal state data D61 by simulation using the physical simulator 5.

[0027] [C-4] Abnormality Location Estimation Unit 44 The abnormality location estimation unit 44 receives the measurement data D42 stored in the memory unit 42 in association with a time axis. Based on the measurement data D42, the abnormality location estimation unit 44 estimates an abnormality location where an abnormality has occurred in a plurality of switching device components that make up the switching device 1, thereby obtaining abnormality location estimation data D44. Like the abnormality occurrence estimation unit 43, the abnormality location estimation unit 44 is configured, for example, by a computing device, and estimates the abnormality location using a program. Here, the abnormality location estimation unit 44 is, for example, a multi-class classifier.

[0028] In this embodiment, the abnormality location estimation unit 44 estimates the abnormality location after the opening and closing operation of the switching device 1 is completed. Furthermore, in this embodiment, the abnormality location estimation unit 44 estimates the abnormality location when the abnormality occurrence estimation unit 43 estimates that an abnormality has occurred in the switching device 1.

[0029] Although details will be described later, the abnormality location estimation unit 44 estimates the abnormality location by machine learning using abnormal state data D62 obtained in advance in association with a time axis regarding the switching operation when the switching device 1 is in an abnormal state as learning data. In this embodiment, the abnormal state data D62 is data obtained using a physical simulator 5 of the switching device 1. Here, information corresponding to a displacement X (see FIG. 1B ) by which the movable contact portion 21 moves relative to the fixed contact portion 22 when the switching device 1 is in an abnormal state is obtained by simulation as the abnormal state data D62.

[0030] [C-5] Lifespan Estimation Unit 50 The lifespan estimation unit 50 receives the normal state feature amount related data D431 obtained by the abnormality occurrence estimation unit 43 and the abnormal state feature amount related data D441 obtained by the abnormality location estimation unit 44. The lifespan estimation unit 50 then estimates the lifespan of the switching device 1 and the lifespan of each of the multiple switching device components that make up the switching device 1 based on the normal state feature amount related data D431 and the abnormal state feature amount related data D441, thereby obtaining lifespan data D50. Like the abnormality occurrence estimation unit 43 and the like, the lifespan estimation unit 50 is configured by, for example, a computing device, and executes lifespan estimation using a program.

[0031] FIG. 3B is a block diagram that schematically shows the function of the life estimation unit 50 in the switching device diagnostic system 4 according to the embodiment.

[0032] 3B , the lifespan estimation unit 50 has a normality score calculation unit 510, an abnormality location score calculation unit 520, and a lifespan calculation unit 530. The normality score calculation unit 510 calculates normality score data SS based on normal state feature amount related data D431. The abnormality location score calculation unit 520 calculates abnormality location score data SD based on abnormal state feature amount related data D441. The lifespan calculation unit 530 calculates lifespan data D50 based on the normality score data SS and the abnormality location score data SD.

[0033] The lifespan estimation unit 50 will be described in detail later.

[0034] [C-6] Estimation Result Integration Unit 45 The estimation result integration unit 45 obtains integrated data D45 by integrating the abnormality occurrence estimation data D43 obtained by the abnormality occurrence estimation unit 43, the abnormality location estimation data D44 obtained by the abnormality location estimation unit 44, and the lifetime data D50 obtained by the lifetime estimation unit 50. The estimation result integration unit 45 is configured by, for example, a computing device, and performs the above integration using a program.

[0035] [C-7] Output Unit 46 The output unit 46 is configured to output the integrated data D45 obtained by the estimation result integration unit 45. The output unit 46 is, for example, an information output device including a display. In addition to the display, the output unit 46 may further include an information output device such as a printer for printing and displaying output.

[0036] [D] Operation of Switchgear Diagnostic System 4 FIG. 4 is a flow diagram showing the operation of the switching gear diagnostic system 4 according to the embodiment.

[0037] As shown in FIG. 4 , the switching device diagnostic system 4 of this embodiment sequentially executes the following steps: input of measurement data D3 (ST41), storage of the measurement data D3 (ST42), estimation of abnormality occurrence (ST43), estimation of the abnormality location (ST44), estimation of the life span (ST50), integration of the estimation results of the abnormality occurrence and the abnormality location (ST45), and output of the integration result (ST46).

[0038] Each operation of the switching device diagnostic system 4 of this embodiment will be described in order with reference to FIG. 4 as well as the above-mentioned FIG. 3A and the like.

[0039] [D-1] Input of Measurement Data D3 (ST41) As shown in FIG. 4, in the switching device diagnostic system 4 of this embodiment, first, input of measurement data D3 (ST41) is executed.

[0040] The input of the measurement data D3 (ST41) is executed by the input unit 41 (see FIG. 3A). The measurement data D3 measured in relation to the opening and closing operation of the opening and closing device 1 is constantly input to the input unit 41 from the sensor 3.

[0041] FIG. 5A is a diagram showing an example of measurement data D3 input to the input unit 41 in the switching device diagnostic system 4 according to the embodiment.

[0042] 5A, the measurement data D3 is, for example, information relating to the displacement X (see FIG. 1B) of the movable contact 21 relative to the fixed contact 22 over time t. Here, the measurement data D3 is an example of data measured when the displacement X1 of the "fully open state (shut-off state)" changes to the displacement X0 of the "closed state (turned-on state)."

[0043] The input unit 41 converts the measurement data D3 into a digital signal having a preset sampling frequency. Specifically, if the sampling frequency of the measurement data D3 input from the sensor 3 is higher than the preset sampling frequency, the measurement data D3 is subjected to, for example, thinning processing. On the other hand, if the sampling frequency of the measurement data D3 input from the sensor 3 is lower than the preset sampling frequency, the measurement data D3 is subjected to, for example, interpolation processing.

[0044] [D-2] Storing the Measurement Data D3 (ST42) Next, as shown in FIG. 4, the measurement data D3 is stored (ST42).

[0045] The measurement data D3 is stored (ST42) in the storage unit 42 (see FIG. 3A).

[0046] FIG. 5B is a diagram showing an example of measurement data D42 stored in the storage unit 42 in the switching device diagnostic system 4 according to the embodiment.

[0047] As shown in Figure 5B, the measurement data D42 stored in the memory unit 42 (solid line portion in Figure 5B) corresponds to a portion of the measurement data D3 (see Figure 5A; dashed line portion in Figure 5B) output from the input unit 41 to the memory unit 42.

[0048] Specifically, the storage unit 42 defines the time t in the measurement data D3 when the displacement X1 decreases by a predetermined threshold to become a displacement X11 as "0." The storage unit 42 then calculates the period from time t1, a predetermined time before the time t of "0," to time t1 after the predetermined time has elapsed. The period from time t1 to time t1 is set to, for example, a period longer than the period when the operating state of the opening and closing device 1 is normal. The storage unit 42 then extracts a portion of the measurement data D3 corresponding to that period (t0 to t1) and stores the extracted measurement data D42 in association with a time axis. Data in the measurement data D3 that was not extracted is, for example, deleted.

[0049] Although not shown, when an operation is performed to change from a displacement X0 of the "closed state (on state)" to a displacement X1 of the "fully open state (off state)," the storage unit 42 similarly stores measurement data D42 obtained by cutting out a portion of the measurement data D3. In other words, the portion of the measurement data D3 obtained when the on operation or the off operation is performed is cut out and stored as measurement data D42.

[0050] [D-3] Estimation of Abnormality Occurrence (ST43) Next, as shown in FIG. 4, estimation of abnormality occurrence (ST43) is performed.

[0051] The estimation of the occurrence of an abnormality (ST43) is performed by the abnormality occurrence estimation unit 43 (see FIG. 3A).

[0052] As described above, the abnormality occurrence estimation unit 43 estimates the presence or absence of an abnormality by machine learning using, as learning data, the normal state data D61 output from the physical simulator 5 of the switching device 1. Here, the normal state data D61 is information corresponding to the displacement X (see FIG. 1B ) by which the movable contact unit 21 moves relative to the fixed contact unit 22 when the switching device 1 is in a normal state.

[0053] In this embodiment, the abnormality occurrence estimation unit 43 obtains, in the feature space, the range of the feature (normal state feature) when the opening and closing device 1 is in a normal state as a feature normal range TS from the normal state data D61, and estimates whether or not an abnormality has occurred based on the feature normal range TS to obtain abnormality occurrence estimation data D43.

[0054] 6A to 6D are diagrams schematically showing an example of how the abnormality occurrence estimation unit 43 determines the feature quantity normal range TS from the normal state data D61 in the switching device diagnostic system 4 according to the embodiment.

[0055] 6A , when determining the feature quantity normal range TS, the abnormality occurrence estimation unit 43 uses a plurality of normal state data D61 output from the physical simulator 5 of the switching device 1. Here, the physical simulator 5 determines the normal state data D61 for each of a plurality of parameters (such as dimensions of components constituting the switching device 1 and operating temperature conditions), and outputs the normal state data D61 determined corresponding to each parameter to the abnormality occurrence estimation unit 43. The normal state data D61 is generated by changing the parameters in the physical simulator 5 within the allowable ranges.

[0056] 6B , the abnormality occurrence estimation unit 43 learns the normal waveform pattern PS when the switching device 1 is in a normal state from the plurality of normal state data D61. Here, an example is shown in which a normal waveform pattern PS1 from time t0 to t11 and a normal waveform pattern PS2 from time t12 to t1 are learned as the normal waveform pattern PS.

[0057] 6C , the abnormality occurrence estimation unit 43 calculates the degree of deviation between the normal waveform pattern PS and the normal state data D61. The degree of deviation is, for example, Euclidean distance, and for each of the multiple normal state data D61, the degree of deviation from the normal waveform pattern PS is calculated as a feature amount (normal state feature amount). Here, for example, a degree of deviation DP1 between the normal waveform pattern PS1 and the normal state data D61, and a degree of deviation DP2 between the normal waveform pattern PS2 and the normal state data D61 are calculated as the feature amount (normal state feature amount) of the normal state data D61.

[0058] 6D , the abnormality occurrence estimation unit 43 calculates a feature normal range TS based on the degrees of deviation DP1, DP2 calculated as the feature amounts (normal state feature amounts) of the plurality of normal state data D61. The feature normal range TS is a range in the feature space classified by the feature amounts (corresponding to the black dots in FIG. 6D ) of the plurality of normal state data D61, and includes the feature amounts of the plurality of normal state data D61.

[0059] 7A and 7B are diagrams schematically illustrating an example of how the abnormality occurrence estimation unit 43 in the switching device diagnostic system 4 according to the embodiment estimates whether or not an abnormality has occurred based on the normal feature range TS.

[0060] When estimating whether or not an abnormality has occurred, as shown in Figure 7A, the abnormality occurrence estimation unit 43 calculates the feature value (first measurement data feature value) of the measurement data D42 by calculating the degree of deviation DP1, DP2 between the measurement data D42 stored in the memory unit 42 and the normal waveform pattern PS.

[0061] Here, as shown in FIG. 7B, for example, a value T421 calculated as the degree of deviation DP1 between the normal waveform pattern PS1 and the measurement data D42, and a value T421 calculated as the degree of deviation DP2 between the normal waveform pattern PS2 and the measurement data D42 are calculated as the feature T42 (first measurement data feature) of the measurement data D42 in the feature space.

[0062] 7B , the abnormality occurrence estimation unit 43 estimates whether the position of the feature quantity T42 of the measurement data D42 is within the feature quantity normal range TS in the feature quantity space. When the feature quantity T42 of the measurement data D42 is within the feature quantity normal range TS in the feature quantity space (see FIG. 7B ), the abnormality occurrence estimation unit 43 estimates that no abnormality has occurred in the switching device 1. On the other hand, when the feature quantity T42 of the measurement data D42 is outside the feature quantity normal range TS in the feature quantity space (not shown), the abnormality occurrence estimation unit 43 estimates that an abnormality has occurred in the switching device 1.

[0063] [D-4] Estimation of Abnormal Location (ST44) Next, as shown in FIG. 4, estimation of abnormal location (ST44) is performed.

[0064] The estimation of the abnormal part (ST44) is performed by the abnormal part estimation unit 44 (see FIG. 3A).

[0065] As described above, the abnormality location estimation unit 44 estimates the abnormality location by machine learning using, as learning data, the abnormal state data D62 output from the physical simulator 5 of the switching device 1. Here, the abnormal state data D62 is information corresponding to the displacement X (see FIG. 1B ) by which the movable contact portion 21 moves relative to the fixed contact portion 22 when the switching device 1 is in an abnormal state.

[0066] In this embodiment, the abnormality location estimation unit 44 obtains, in the feature space, a range of feature values ​​when each of the plurality of switching device components is in an abnormal state as a feature value abnormality range TD in association with the plurality of switching device components. Then, the abnormality location estimation unit 44 estimates an abnormality location based on the plurality of feature value abnormality ranges TD obtained for each of the plurality of switching device components, and obtains abnormality location estimation data D44.

[0067] 8A to 8D are diagrams schematically showing an example of how the abnormality location estimation unit 44 determines the feature amount abnormal range TD from the abnormal state data D62 in the switching device diagnosis system 4 according to the embodiment.

[0068] When calculating the feature quantity abnormality range TD, the abnormality location estimation unit 44 uses a plurality of abnormal state data D62 output from the physical simulator 5 of the switching device 1, as shown in Fig. 8A . Here, the physical simulator 5 calculates the abnormal state data D62 for each of a plurality of parameters (e.g., dimensions of components constituting the switching device 1, operating temperature conditions, etc.), and outputs the abnormal state data D62 calculated corresponding to each parameter to the abnormality location estimation unit 44. The abnormal state data D62 is generated by changing the parameters outside the allowable range in the physical simulator 5. The abnormal state data D62 is generated for each of a plurality of switching device components, and Fig. 8A illustrates, as an example, a plurality of abnormal state data D62a for a first switching device component and a plurality of abnormal state data D62b for a second switching device component.

[0069] 8B , the abnormal part estimation unit 44 learns, from the plurality of abnormal state data D62, an abnormal waveform pattern PD when each of the plurality of switching device components is in an abnormal state. Here, an example is shown in which an abnormal waveform pattern PD1 between time t0 and time t11d and an abnormal waveform pattern PD2 between time t12d and time t1d are learned as the abnormal waveform patterns PD.

[0070] 8C , the abnormal portion estimation unit 44 calculates the degree of deviation between the abnormal waveform pattern PD and the abnormal state data D62. The degree of deviation is, for example, Euclidean distance, and for each of the multiple abnormal state data D62, the degree of deviation from the abnormal waveform pattern PD is calculated as a feature amount (abnormal state feature amount). Here, for example, the degree of deviation DP1b between the abnormal waveform pattern PD1 and the abnormal state data D62a, and the degree of deviation DP2b between the abnormal waveform pattern PD2 and the abnormal state data D62a are calculated as the feature amount (abnormal state feature amount) of the abnormal state data D62a. Although not shown in the figure, the feature amount of the abnormal state data D62b is also calculated in the same way as the feature amount of the abnormal state data D62a.

[0071] Then, as shown in FIG. 8D , the abnormality location estimation unit 44 calculates a feature abnormality range TD based on the degrees of deviation DP1b, DP2b calculated as the feature amounts (abnormal state feature amounts) of the plurality of abnormal state data D62. The feature abnormality range TD is a range in the feature space classified by the feature amounts (corresponding to the black dots in FIG. 8D ) of the plurality of abnormal state data D62, and includes the feature amounts of the plurality of abnormal state data D62. The feature abnormality range TD is calculated for each of the plurality of switching device components. FIG. 8D illustrates an example in which a feature abnormality range TD1 for a first switching device component and a feature abnormality range TD2 for a second switching device component are calculated as the feature abnormality range TD.

[0072] 9A and 9B are diagrams schematically illustrating an example of how the abnormality location estimation unit 44 estimates the abnormality location based on the feature amount abnormality range TD in the switching device diagnostic system 4 according to the embodiment.

[0073] When estimating the abnormal location, as shown in FIG. 9A, the abnormal location estimation unit 44 calculates the feature of the measurement data D42 (second measurement data feature) by calculating the degree of deviation DP1b, DP2b between the measurement data D42 stored in the memory unit 42 and the abnormal waveform pattern PD.

[0074] Here, as shown in FIG. 9B, for example, a value TD421 calculated as the degree of deviation DP1b between the abnormal waveform pattern PD1 and the measurement data D42, and a value TD422 calculated as the degree of deviation DP2b between the abnormal waveform pattern PD2 and the measurement data D42 are calculated as the feature TD42 (second measurement data feature) of the measurement data D42 in the feature space.

[0075] 9B , the abnormality location estimation unit 44 estimates whether the position of the feature value TD42 (second measurement data feature value) of the measurement data D42 is within a feature value abnormality range TD in the feature value space. When the feature value TD42 of the measurement data D42 is within a feature value abnormality range TD1 among the multiple feature value abnormality ranges TD in the feature value space, the abnormality location estimation unit 44 estimates that the first switching device component is the abnormality location. When the feature value TD42 of the measurement data D42 is within a feature value abnormality range TD2 among the multiple feature value abnormality ranges TD in the feature value space, the abnormality location estimation unit 44 estimates that the second switching device component is the abnormality location (not shown).

[0076] Although not shown in the figure, if the feature of the measurement data D42 is not within the multiple feature abnormality ranges TD for each of the multiple switching device components, the abnormality location estimation unit 44 determines whether the degree of deviation between the feature of the measurement data D42 and the feature of the measurement data D42 for the multiple switching device components is equal to or less than a predetermined threshold value.

[0077] Then, among the feature amount abnormality ranges TD related to the plurality of switching gear components, if there is one feature amount abnormality range TD in which the degree of deviation from the feature amount of the measurement data D42 is equal to or less than a predetermined threshold, the switching gear component related to that one feature amount abnormality range TD is estimated to be the abnormal location (not shown). For example, among the feature amount abnormality ranges TD1 and TD2, if the degree of deviation from the feature amount abnormality range TD1 is equal to or less than a threshold, but the degree of deviation from the feature amount abnormality range TD2 exceeds the threshold, the first switching gear component is estimated to be the abnormal location.

[0078] Furthermore, when the feature quantities of the measurement data D42 are not within the plurality of feature quantity abnormality ranges TD for each of the plurality of switching device components, and all of the degrees of deviation between each of the feature quantity abnormality ranges for the plurality of switching device components and the feature quantities of the measurement data D42 exceed a threshold value (not shown), the abnormality location estimation unit 44 estimates that the abnormality location is unknown (not shown). Note that the degrees of deviation are, for example, Euclidean distances.

[0079] [D-5] Estimation of Lifespan (ST50) Next, as shown in FIG. 4, estimation of lifespan (ST50) is performed.

[0080] The lifetime estimation (ST50) is performed in the lifetime estimation unit 50 (see FIG. 3A).

[0081] As described above, the lifespan estimation unit 50 includes the normality score calculation unit 510, the abnormality portion score calculation unit 520, and the lifespan calculation unit 530 (see FIG. 3B).

[0082] In the lifespan estimation unit 50, the normal score calculation unit 510 calculates normal score data SS based on the normal state feature amount related data D431 (see FIG. 3B ). The normal score calculation unit 510 receives, as the normal state feature amount related data D431, the feature amount normal range TS and the feature amount T42 (first measurement data feature amount) of the measurement data D42 from the abnormality occurrence estimation unit 43. Then, the normal score calculation unit 510 calculates the normal score data SS based on the feature amount normal range TS and the feature amount T42 of the measurement data D42.

[0083] FIG. 10A is a diagram schematically illustrating an example of how the normal score calculation unit 510 calculates the normal score data SS in the switching device diagnostic system 4 according to the embodiment.

[0084] 10A , when the feature T42 of the measurement data D42 is located within the feature normal range TS, the normal score calculation unit 510 calculates the degree of deviation of the feature T42 of the measurement data D42 from the boundary of the feature normal range TS as normal score data SS. The degree of deviation is, for example, Euclidean distance, and the smallest distance between the boundary of the feature normal range TS and the feature T42 of the measurement data D42 is calculated as normal score data SS.

[0085] Furthermore, in the lifespan estimation unit 50, the abnormality location score calculation unit 520 calculates abnormality location score data SD based on the abnormal state feature amount related data D441 (see FIG. 3B ). The abnormality location score calculation unit 520 receives the feature amount abnormal range TD and the feature amount TD42 (second measurement data feature amount) of the measurement data D42 as input from the abnormality location estimation unit 44. Then, the abnormality location score calculation unit 520 calculates the abnormality location score data SD based on the feature amount abnormality range TD and the feature amount TD42 of the measurement data D42.

[0086] FIG. 10B is a diagram schematically illustrating an example of how the abnormality location score calculation unit 520 calculates the abnormality location score data SD in the switching device diagnosis system 4 according to the embodiment.

[0087] 10B , when the feature TD42 of the measurement data D42 is located outside the feature abnormality range TD (TD1, TD2), the abnormal location score calculation unit 520 calculates the degree of deviation of the feature TD42 of the measurement data D42 from the boundary of the feature abnormality range TD (TD1, TD2) as the abnormal location score data SD. The degree of deviation is, for example, Euclidean distance, and the shortest distance between the boundary of the feature abnormality range TD (TD1, TD2) and the feature TD42 of the measurement data D42 is calculated as the abnormal location score data SD. When there are multiple feature abnormality ranges TD (e.g., TD1, TD2), the abnormal location score data SD is calculated for each of the multiple feature abnormality ranges TD.

[0088] Thereafter, in the lifespan estimation unit 50, the lifespan calculation unit 530 calculates lifespan data D50 based on the normality score data SS and the abnormality location score data SD (see FIG. 3B).

[0089] Here, the life calculation unit 530 estimates the life of the switching device based on the transition of the normal score data SS obtained by the normal score calculation unit 510, and obtains the switching device life estimation data D530a as the life data D50.

[0090] 11A is a diagram schematically illustrating an example of how the life calculation unit 530 calculates the switching device life estimation data D530a in the switching device diagnostic system 4 according to the embodiment. In Fig. 11A, the vertical axis represents the value of the normal score data SS, and the horizontal axis represents the number of times the switching operation has been performed in the switching device 1. The horizontal axis may also be a time axis.

[0091] 11A , the life calculation unit 530 calculates an approximate expression FSS relating to the transition of the normal score data SS. The approximate expression FSS is calculated, for example, by the least squares method. Then, the life calculation unit 530 calculates the number of opening and closing operations corresponding to the threshold value SSth of the normal score data SS in the approximate expression FSS as the switching device life estimation data D530a.

[0092] In addition, the life calculation unit 530 estimates the lifespans of multiple switching device components based on the trends in the abnormality point score data SD calculated by the abnormality point score calculation unit 520, thereby calculating switching device component lifespan estimation data D530b as lifespan data D50.

[0093] 11B is a diagram schematically illustrating an example of how the life calculation unit 530 calculates the switching device component life estimation data D530b in the switching device diagnostic system 4 according to the embodiment. In Fig. 11B, the vertical axis represents the value of the abnormality location score data SD, and the horizontal axis represents the number of times the switching operation has been performed in the switching device 1. The horizontal axis may also be a time axis.

[0094] 11B , the life calculation unit 530 calculates an approximate expression FSD relating to the transition of the abnormality location score data SD. The approximate expression FSD is calculated, for example, by the least squares method. Then, the life calculation unit 530 calculates the number of switching operations corresponding to the threshold value SDth of the abnormality location score data SD in the approximate expression FSD as the switching device component life estimation data D530b.

[0095] [D-6] Integration of Estimated Results of Abnormal Occurrence and Abnormal Location (ST45) Next, as shown in FIG. 4, the estimation results of abnormality occurrence, abnormal location, and life span are integrated (ST45).

[0096] The estimation results of abnormality occurrence, abnormality location, and life span are integrated (ST45) by an estimation result integration unit 45 (see FIG. 3A). The estimation result integration unit 45 integrates the abnormality occurrence estimation data D43 obtained by the abnormality occurrence estimation unit 43, the abnormality location estimation data D44 obtained by the abnormality location estimation unit 44, and the life span data D50 obtained by the life span estimation unit 50 to obtain integrated data D45.

[0097] Specifically, when the abnormality occurrence estimation unit 43 estimates that no abnormality has occurred, the estimation result integration unit 45 integrates the estimation result of no abnormality with the estimation result of no abnormal location. When the abnormality occurrence estimation unit 43 estimates that no abnormality has occurred and the abnormal location estimation unit 44 estimates that one of the multiple switching device components is the abnormal location, the estimation result integration unit 45 integrates the estimation result of an abnormality with the estimation result that the abnormal location is one switching device component. When the abnormality occurrence estimation unit 43 estimates that no abnormality has occurred and the abnormal location estimation unit 44 estimates that an unknown switching device component other than the multiple switching device components is the abnormal location, the estimation result integration unit 45 integrates the estimation result of an abnormality with the estimation result that the abnormal location is an unknown switching device component. In addition to the above, the estimation result integration unit 45 further integrates the lifetime estimation results obtained by the life estimation unit 50.

[0098] [D-7] Output of Estimation Results (ST46) Next, as shown in FIG. 4, output of the integration results (ST46) is executed.

[0099] The output of the estimation result (ST46) is performed by the output unit 46 (see FIG. 3A).

[0100] The output unit 46 displays, for example, an image relating to the integrated data D45 obtained by the estimation result integration unit 45 on a display screen.

[0101] FIG. 12 is a diagram schematically showing an example of an image related to the integrated data D45 output by the output unit 46 in the switching device diagnostic system 4 according to the embodiment.

[0102] As shown in FIG. 12, an image M45 relating to the integrated data D45 includes an image M451 relating to the estimated result of abnormality occurrence, an image M452 relating to the estimated result of the abnormality location, and an image M454 relating to the estimated result of the lifespan.

[0103] Specifically, when the estimation result of the abnormality occurrence indicates that an abnormality is present, the image M451 relating to the estimation result of the abnormality occurrence is, for example, text information of "Operating status: Abnormal". Although not shown in the figure, when the estimation result of the abnormality occurrence indicates that an abnormality is not present, text information of "Operating status: Normal" is displayed as the image M451 relating to the estimation result of the abnormality occurrence.

[0104] The image M452 regarding the estimation result of the abnormal location includes, for example, if the estimation result indicates that part Y (first opening / closing device component) is the abnormal location, text information M452a stating "Abnormality in part Y" and a schematic diagram M452b emphasizing the position of part Y in the opening / closing device 1 (the area indicated by the dashed dotted line in Figure 12).

[0105] In addition, the image M452 relating to the estimation result of the abnormality location may include text information M432c such as "inspection and replacement of part Y" as maintenance and inspection information. The maintenance and inspection information may include, for example, inspection items corresponding to the estimation result of the abnormality location. In addition, the maintenance and inspection information may include arrangement information for parts to be arranged for the maintenance and inspection work. Although not shown in the figure, if the estimation result of the abnormality occurrence is that no abnormality exists, text information M432c such as "none" is displayed as the maintenance and inspection information.

[0106] The image M454 relating to the lifespan estimation result includes, for example, the remaining number of opening and closing operations estimated to result in an abnormality occurring for the opening and closing device 1 and a plurality of opening and closing device components (component X, component Y, etc.) that make up the opening and closing device 1. Note that if it is estimated that an abnormality exists at the present time, the remaining number of opening and closing operations is displayed as "0 times."

[0107] [E] Summary As described above, the switchinggear diagnostic system 4 of this embodiment diagnoses the operating state of the switchinggear 1, which is configured with a plurality of switchinggear components to perform switching operations, each time a switching operation is performed. In the switchinggear diagnostic system 4 of this embodiment, in diagnosing the operating state of the switchinggear 1, the abnormality occurrence estimation unit 43 estimates whether or not an abnormality has occurred in the switchinggear 1 to obtain abnormality occurrence estimation data D43, and the abnormality location estimation unit 44 estimates an abnormality location where an abnormality has occurred in the plurality of switchinggear components to obtain abnormality location estimation data D44. Then, the lifespan estimation unit 50 estimates the lifespan of the switchinggear 1 and the lifespan of the switchinggear components to obtain lifespan data D50.

[0108] The abnormality occurrence estimation unit 43 estimates whether an abnormality has occurred based on the relationship between a feature value T42 (first measurement data feature value) of the measurement data D42 obtained from the degree of deviation between the measurement data D42 and the normal waveform pattern PS and a feature value normal range TS. The abnormality location estimation unit 44 estimates the abnormality location based on the relationship between a feature value TD42 (second measurement data feature value) of the measurement data D42 obtained from the degree of deviation between the measurement data D42 and the abnormal waveform pattern PD and an abnormal feature value range TD.

[0109] The lifespan estimation unit 50 has a normality score calculation unit 510, an abnormality location score calculation unit 520, and a lifespan calculation unit 530. When a feature quantity T42 (first measurement data feature quantity) of measurement data D42 is located within a feature quantity normal range TS, the normality score calculation unit 510 calculates the degree of deviation of the feature quantity T42 from the boundary of the feature quantity normal range TS as normality score data SS. When a feature quantity TD42 (second measurement data feature quantity) of measurement data D42 is located outside the feature quantity abnormal range TD, the abnormality location score calculation unit 520 calculates the degree of deviation of the feature quantity TD42 from the boundary of the feature quantity abnormal range TD as abnormality location score data SD. The life calculation unit 530 estimates the life of the switching device based on the trend of the normal score data D51, thereby obtaining switching device life estimation data D530a as life data D50, and also estimates the life of multiple switching device components based on the trend of the abnormality location score data SD, thereby obtaining switching device component life estimation data D530b as life data D50.

[0110] The estimation result integration unit 45 integrates the abnormality occurrence estimation data D43, the abnormality location estimation data D44, and the lifespan data D50 to obtain integrated data D45, and then the output unit 46 outputs the integrated data D45.

[0111] As a result, in this embodiment, in addition to detecting signs of an abnormality in the switching device, it is possible to easily and accurately grasp the estimated location of the abnormality and the lifespan of the switching device. As a result, according to this embodiment, it is possible to replace parts, etc. before the current interruption capability of the switching device 1 is adversely affected. Furthermore, because the work of identifying the abnormality location can be simplified, it is possible to shorten the time required for part replacement work and the downtime required to stop the switching device 1 for replacement work, etc.

[0112] In this embodiment, the abnormality occurrence estimation unit 43 estimates the presence or absence of an abnormality by machine learning using normal state data D61 obtained in advance in association with a time axis regarding the opening and closing operation when the switching device 1 is in a normal state as learning data. Furthermore, the abnormality location estimation unit 44 estimates the abnormal location by machine learning using abnormal state data D62 obtained in advance in association with a time axis regarding the opening and closing operation when the switching device 1 is in an abnormal state as learning data. Similarly, the normal feature range TS and the abnormal feature range TD obtained by machine learning are used to estimate the lifespan of the switching device. Therefore, in this embodiment, it is possible to estimate the occurrence of an abnormality, the abnormal location, and the lifespan with high sensitivity.

[0113] In the present embodiment, the normal state data D61 and the abnormal state data D62 are data obtained using the physical simulator 5 of the switching device 1. Therefore, in the present embodiment, the normal state data D61 and the abnormal state data D62 required for machine learning can be sufficiently obtained without operating the actual switching device 1 to perform machine learning.

[0114] <Modifications> Modifications of the above embodiment will now be described.

[0115] [A] Modification 1 In the above embodiment, the case where the diagnosis target of the switching gear diagnostic system 4 is one switching gear 1 has been exemplified, but this is not limiting. The diagnosis target of the switching gear diagnostic system 4 may be a plurality of switching gears 1. In this case, the output unit 46 of the switching gear diagnostic system 4 outputs the integrated data D45 acquired for each of the plurality of switching gears 1. At this time, the output unit 46 displays, for example, an image in which each of the integrated data D45 of the plurality of switching gears 1 is lined up on the screen of the display.

[0116] FIG. 13 is a diagram schematically showing an example of an image related to the integrated data D45 output by the output unit 46 in the switching device diagnostic system 4 according to the first modification.

[0117] 13 illustrates an example in which the switching devices 1 to be diagnosed by the switching device diagnostic system 4 are a first switching device 1a and a second switching device 1b. In this case, as shown in FIG. 13, the image M45 related to the integrated data D45 includes, for the first switching device 1a and the second switching device 1b, an image M451 related to the estimated result of abnormality occurrence, an image M452 related to the estimated result of the abnormality location, and an image M454 related to the estimated result of the lifespan, as in the case of the above embodiment (see FIG. 12). In addition, as shown in FIG. 13, the image M45 related to the integrated data D45 may include an image M453 that summarizes the estimation results in a table format.

[0118] Therefore, in this modification, even if the diagnosis target is a plurality of switching devices 1, it is possible to quickly and accurately grasp the estimation results for the plurality of switching devices 1.

[0119] Note that, as in this modified example, when the switching device diagnostic system 4 is to diagnose multiple switching devices 1, the output unit 46 may output the image M45 so that, for example, the estimation result for one switching device 1 out of the multiple switching devices 1 is displayed preferentially over the estimation results for other switching devices 1. For example, if the numerical value related to the lifespan of one switching device 1 is smaller than the numerical value related to the lifespan of the other switching devices 1, the estimation result for one switching device 1 may be displayed preferentially over the estimation results for the other switching devices 1.

[0120] [B] Modification 2 In the above embodiment, the switchinggear diagnostic system 4 may be configured using a cloud system. This eliminates the need to install the switchinggear diagnostic system 4, which is configured from a physical computing device or the like, near the switchinggear 1, making it possible to easily diagnose the operating state of the switchinggear 1.

[0121] [C] Modification 3 In the above embodiment, the abnormality occurrence estimation unit 43 may be configured to perform an operation using a convolution kernel on the measurement data D42 stored in the storage unit 42, and then estimate the presence or absence of an abnormality. The operation using the convolution kernel may be performed multiple times. In other words, the result of an aggregation operation performed by applying multiple convolution kernels to the measurement data D42 may be obtained as the feature quantity of the measurement data D42. In this case, the accuracy of the feature quantity of the measurement data D42 is improved, making it possible to accurately estimate the presence or absence of an abnormality.

[0122] [D] Modification 4 In the above embodiment, the switchgear 1 to be diagnosed by the switchgear diagnostic system 4 is a circuit breaker, but this is not limiting. The switchgear 1 may be a device other than a circuit breaker, such as a disconnecting switch or a grounding switch.

[0123] In the above embodiment, the operating mechanism 12 of the opening and closing device 1 is a spring operating mechanism, but this is not limiting. The operating mechanism 12 may be another mechanism such as a hydraulic operating mechanism.

[0124] [E] Modification 5 In the above embodiment, the measurement data D3 is information relating to the displacement X (see FIG. 1B ) of the movable contact portion 21 relative to the fixed contact portion 22 over time t. However, this is not limiting. The measurement data D3 (time-series data) may be other data as long as it is information measured regarding the opening and closing operation of the opening and closing device 1.

[0125] For example, in the case where the operation mechanism unit 12 performs the opening and closing operation of the switching device 1 using electrical equipment such as an electromagnetic actuator (not shown) or an electric motor (not shown), the current measured in the electrical equipment such as the electromagnetic actuator (not shown) or the electric motor (not shown) may be used as the measurement data D3. Furthermore, the acceleration measured using an acceleration sensor (not shown) during the opening and closing operation of the switching device 1 may be used as the measurement data D3. Furthermore, the sound intensity measured using a sound sensor (not shown) during the opening and closing operation of the switching device 1 may be used as the measurement data D3. Other operating environment values ​​(control voltage, operating oil pressure, temperature, time interval from the previous operation) may also be used as the measurement data D3.

[0126] [F] Modification 6 In the above embodiment, the abnormality location estimation unit 44 calculates the characteristic quantity abnormality range TD for two switching device components, but this is not limiting. The characteristic quantity abnormality range TD may be calculated for three or more switching device components, or the characteristic quantity abnormality range TD may be calculated for a single switching device component.

[0127] [G] Modification 7 In the above embodiment, the deviation degree is the Euclidean distance, but this is not limiting. The deviation degree may be a DTW (Dynamic Time Warping) distance or the like.

[0128] [H] Modification 8 In the above embodiment, the image M452 relating to the estimation result of the abnormal location shows information about the switching device component (component Y) estimated to be the abnormal location, but this is not limiting. Information about switching device components other than the switching device component (component Y) estimated to be the abnormal location may also be output as the image M452 relating to the estimation result of the abnormal location.

[0129] <Others> Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are intended to be included in the scope of the invention and its equivalents as defined in the claims, as well as in the scope and spirit of the invention.

[0130] 1: switching device, 1a: first switching device, 1b: second switching device, 3: sensor, 4: switching device diagnostic system, 5: physical simulator, 11: switching device main body, 12: operation mechanism, 20: pressure vessel, 21: moving contact, 22: fixed contact, 41: input unit, 42: memory unit, 43: abnormality occurrence estimation unit, 44: abnormality location estimation unit, 45: estimation result integration unit, 46: output unit, 50: life estimation unit, 510: normality score calculation unit, 520: abnormality location score calculation unit, 530: life calculation unit, D3: measurement data, D42: measurement data, D43: abnormality occurrence estimation data, D431: normal state feature amount related data, D44: abnormality location estimation data, D441: abnormality state feature amount related data, D45: integrated data, D50: life data, D51: normality score data, D530a: switching device life Estimation data, D530b: switchgear component life estimation data, D61: normal state data, D62: abnormal state data, D62a: abnormal state data, D62b: abnormal state data, DP1: deviation degree, DP1b: deviation degree, DP2: deviation degree, DP2b: deviation degree, PD: abnormal waveform pattern, PD1: abnormal waveform pattern, PD2: abnormal waveform pattern, PS: normal waveform pattern, PS1: normal waveform pattern, PS2: normal waveform pattern, SD: abnormal location score data, SDth: threshold value, SS: normal score data, SSth: threshold value, T42: feature amount (first measurement data feature amount), TD: feature amount abnormal range, TD1: feature amount abnormal range, TD2: feature amount abnormal range, TD42: feature amount (second measurement data feature amount), TS: feature amount normal range, X: displacement

Claims

1. A switchgear diagnostic system for diagnosing the operating state of a switchgear configured with a plurality of switchgear components to perform a switching operation, comprising: a storage unit that stores measurement data measured regarding the switching operation while the switchgear is performing the switching operation, in association with a time axis; an abnormality occurrence estimation unit that obtains abnormality occurrence estimation data by performing an estimation of whether or not an abnormality has occurred in the switchgear through machine learning using, as learning data, normal state data obtained in advance regarding the switching operation and associated with the time axis when the switchgear is in a normal state; an abnormality location estimation unit that obtains abnormality location estimation data by performing an estimation of an abnormal location where an abnormality has occurred in the plurality of switchgear components through machine learning using, as learning data, abnormal state data obtained in advance regarding the switching operation and associated with the time axis when the switchgear is in an abnormal state; a lifespan estimation unit that obtains lifespan data by estimating the lifespan of the switchgear and the lifespan of the switchgear components; and an estimation result integration unit that obtains integrated data by integrating the abnormality occurrence estimation data obtained by the abnormality occurrence estimation unit, the abnormality location estimation data obtained by the abnormality location estimation unit, and the lifespan data obtained by the lifespan estimation unit. an output unit that outputs the integrated data obtained by the estimation result integration unit, wherein the abnormality occurrence estimation unit learns a normal waveform pattern when the switching device is in a normal state from the plurality of normal state data, determines a normal range of feature amounts by obtaining a degree of deviation between the normal waveform pattern and each of the plurality of normal state data as a feature amount, obtains a first measurement data feature amount by obtaining a degree of deviation between the measurement data stored in the storage unit and the normal waveform pattern, and performs an estimation of whether or not an abnormality has occurred in the switching device based on the position of the first measurement data feature amount and the normal range of feature amounts,the abnormality location estimation unit learns an abnormal waveform pattern when each of the plurality of switching device components is in an abnormal state from the plurality of abnormal state data, determines a feature abnormality range by obtaining a degree of deviation between the abnormal waveform pattern and each of the plurality of abnormal state data as a feature, obtains a second measurement data feature by obtaining a degree of deviation between the measurement data stored in the storage unit and the abnormal waveform pattern, and performs estimation of an abnormal location where an abnormality has occurred in the plurality of switching device components based on the position of the second measurement data feature and the feature abnormality range; the life estimation unit includes: a normal score calculation unit that, when the first measurement data feature is located within the feature normal range, obtains a degree of deviation of the first measurement data feature from a boundary of the feature normal range as normal score data; an abnormality location score calculation unit that, when the second measurement data feature is located outside the feature abnormality range, calculates the degree of deviation of the second measurement data feature from the boundary of the feature abnormality range as abnormality location score data; and a lifespan calculation unit that estimates a lifespan of the switching device based on a transition of the normality score data calculated by the normality score calculation unit to calculate switching device life estimation data as the lifespan data, and that estimates lifespans of the plurality of switching device components based on the transition of the abnormality location score data calculated by the abnormality location score calculation unit to calculate switching device component life estimation data as the lifespan data.

2. The switching device diagnostic system according to claim 1, wherein the life estimation unit determines the switching device life estimation data each time the switching operation is performed.

3. The switchgear diagnostic system according to claim 1, wherein the life estimation unit determines the switchgear component life estimation data each time the switching operation is performed.

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