WO2022130626A1 - Abnormality diagnosis device, program and abnormality diagnosis method - Google Patents

Abnormality diagnosis device, program and abnormality diagnosis method Download PDF

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Publication number
WO2022130626A1
WO2022130626A1 PCT/JP2020/047483 JP2020047483W WO2022130626A1 WO 2022130626 A1 WO2022130626 A1 WO 2022130626A1 JP 2020047483 W JP2020047483 W JP 2020047483W WO 2022130626 A1 WO2022130626 A1 WO 2022130626A1
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operating state
feature amount
abnormality diagnosis
setting information
electric device
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PCT/JP2020/047483
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French (fr)
Japanese (ja)
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新治 村田
久幸 折田
広考 高橋
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株式会社日立製作所
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Priority to PCT/JP2020/047483 priority Critical patent/WO2022130626A1/en
Publication of WO2022130626A1 publication Critical patent/WO2022130626A1/en

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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R21/00Arrangements for measuring electric power or power factor

Definitions

  • the present invention relates to an abnormality diagnosis device, a program, and an abnormality diagnosis method for electrical equipment.
  • NILM Non-Intrusive Load Monitoring
  • Patent Document 1 a measurement sensor installed near the power supply line inlet of a power consumer and data extraction for extracting data on the currents of fundamental waves and harmonics and their phases with respect to their voltages from the measurement data detected by the measurement sensors.
  • a means and a pattern recognition means for estimating the operating state of the electric equipment used by the electric power consumer based on the data on the currents of the fundamental waves and harmonics from the data extraction means and the phase with respect to their voltages.
  • An electrical equipment monitoring system characterized by being equipped is described.
  • Patent Document 2 describes a device state detecting device for detecting the state of one or a plurality of devices, which is a measuring means for measuring a physical quantity of an environment in which the device is installed, and a measured value measured by the measuring means.
  • a feature amount calculation means for calculating the feature amount of the above, a storage means for storing the feature amount for each device and the corresponding device state as dictionary data in advance, and a feature amount calculated by the feature amount calculation means are searched.
  • the device state is characterized by having a device state detecting means for searching the feature amount stored in the dictionary data as a key and detecting the device state based on the device state corresponding to the feature amount specified as the search result. The detector is described.
  • Patent Document 1 and Patent Document 2 in order to estimate (monitor) the operating state, it is necessary to give and learn the operating state of the electric device as teacher data in advance.
  • data on the operating state of the electric device in the abnormal state is required, but it is often difficult to acquire data on the abnormal state of the electric device.
  • the present invention has been made in view of such a background, and provides an abnormality diagnosis device, a program, and an abnormality diagnosis method capable of diagnosing an abnormality even when there is no data at the time of abnormality of an electric device. Is the subject.
  • the abnormality diagnosis device includes a power data collection unit that collects power data of an electric device, a feature amount extraction unit that acquires a feature amount from the collected power data, and the above-mentioned.
  • the operating state feature amount data including the relationship between the operating state of the electric device and the feature amount of the power data in the operating state, the operating state estimation unit that estimates the operating state of the electric device from the acquired feature amount, and the electricity.
  • a setting information collection unit that collects setting information that sets the operating state of the device, an abnormality diagnosis unit that determines that the electrical device is abnormal if the estimated operating state does not correspond to the collected setting information, To prepare for.
  • an abnormality diagnosis device a program, and an abnormality diagnosis method capable of diagnosing an abnormality even when there is no data at the time of abnormality of an electric device. Issues, configurations and effects other than those described above will be clarified by the description of the following embodiments.
  • the abnormality diagnosis device acquires the power data used by the electric device and the setting information of the electric device.
  • the setting information is information for setting various operating states for an electric device, and for example, in an air conditioner, there are stop, cooling, heating, dehumidification, temperature setting, wind direction, and the like.
  • the abnormality diagnosis device estimates the operating state of the electric device from the power data, and if it is different from the acquired setting information, determines that it is an abnormality. In order to estimate the abnormal state by the conventional technique, it is necessary to collect the power data of the electric device in the abnormal state.
  • the number of abnormal states is not limited to one, and it is difficult to collect power data of all electric devices in abnormal states including unexpected ones.
  • the operating state estimated from the power data in the embodiment of the present invention is the normal operating state of the electrical equipment. Therefore, it is easy to collect the data necessary for estimation (features that characterize the operating state), and it is possible to diagnose abnormalities in electrical equipment at low cost.
  • FIG. 1 is an overall configuration diagram of the abnormality diagnosis system 10 according to the first embodiment.
  • the abnormality diagnosis system 10 includes a measuring device 300 and an abnormality diagnosis device 100 (see FIG. 2 described later).
  • the measuring device 300 measures the electric power supplied from the power source 400 to the electric device 200, and transmits the measured electric power data to the abnormality diagnosis device 100 via the network 500.
  • the measuring device 300 may be a device or system having measurement data, for example, an energy management system.
  • the abnormality diagnosis device 100 acquires setting information from the electric device 200 via the network 500.
  • the electric device 200 is a device that operates by using electric power, and is, for example, an air conditioner, a refrigerator, a lighting fixture, an electric water heater, and the like.
  • the power source 400 is a power supply source, and supplies power to the electric device 200 via a power line such as a power system or a premises distribution network.
  • the electric power data is data related to electric power, and is an instantaneous value such as electric energy (WH), electric power consumption (W), voltage (V), current (A), power factor, and time series value.
  • the network 500 is used for transmitting power data and setting information.
  • the network 500 may use a communication network such as a public communication network or a private communication network regardless of whether it is wired or wireless.
  • FIG. 2 is a functional block diagram of the abnormality diagnosis device 100 according to the first embodiment.
  • the abnormality diagnosis device 100 includes a control unit 110, a storage unit 130, and a communication unit 180.
  • the communication unit 180 is composed of a communication device such as a NIC (Network Interface Card), and transmits / receives communication data to / from other devices including the electric device 200 and the measuring device 300.
  • the storage unit 130 is composed of a storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), and a flash memory.
  • the storage unit 130 stores the operating state feature amount database 140 (described as the operating state feature amount DB (database) in FIG. 2, see FIG. 3 described later) and the program 131.
  • the program 131 describes the procedure for the abnormality diagnosis process (see FIG. 4 described later).
  • FIG. 3 is a data structure diagram of the operating state feature amount database 140 according to the first embodiment.
  • the operating state feature amount database 140 stores the operating state of the electric device 200 and the feature amount of the electric power data in the operating state in association with each other.
  • the operating state feature amount database 140 is tabular data, and one row (record) includes an operating state 141, an average value 142, a time series value 143, and a column (attribute) of a frequency distribution 144.
  • the operating state 141 is an operating state of the electric device 200, and there are states (set state, operating state) such as "OFF" (stop), "dehumidification", and "cooling".
  • the average value 142 and the time series value 143 are the average and time series value (time series data) of the electric power data when the electric device 200 is in the operating state 141.
  • the frequency distribution 144 is, for example, a frequency distribution obtained by wavelet transforming or discrete Fourier transforming power data having one or two cycles (20 to 40 ms for a commercial power supply of 50 Hz).
  • the power data is, as described above, power consumption, power consumption, voltage, current, power factor, and the like.
  • the feature amount may be acquired from the electric device 200 in advance, or data of the same type of electric device may be used.
  • the electric device of the same type may be, for example, a model such as an air conditioner or a refrigerator, an air conditioner having the same power consumption, or an air conditioner having the same model number.
  • it may be acquired from a server (not shown) connected to the network 500 (see FIG. 1) and storing the feature amount.
  • the feature amount on the recording medium connected to the input / output unit (not shown) of the abnormality diagnosis device 100 may be copied and acquired.
  • the control unit 110 includes a power data collection unit 111, a feature amount extraction unit 112, an operating state estimation unit 113, a setting information collection unit 114, an abnormality diagnosis unit 115, and a notification unit 116.
  • the power data collection unit 111 acquires power data from the measuring device 300 (see FIG. 1) and outputs the power data to the feature amount extraction unit 112.
  • the feature amount extraction unit 112 extracts, for example, an average value, a time series value, and a frequency distribution as feature amounts from the power data.
  • the operating state estimation unit 113 searches for a record having a feature amount most similar to the feature amount extracted by the feature amount extracting unit 112 among the records of the operating state feature amount database 140 (see FIG. 3).
  • the operating state estimation unit 113 outputs the operating state 141 of the search result record to the abnormality diagnosis unit 115 as the estimation result of the operating state of the electric device 200.
  • the operating state estimation unit 113 outputs, for example, the operating state 141 of the record having the highest correlation between the time series value and the frequency distribution as the estimation result.
  • the setting information collecting unit 114 acquires the setting information for setting the operating state from the electric device 200 and outputs it to the abnormality diagnosis unit 115.
  • the abnormality diagnosis unit 115 compares the operating state estimated by the operating state estimation unit 113 with the setting information acquired by the setting information collecting unit 114, and if there is a difference, determines that the electric device 200 is abnormal. If there is no difference, the abnormality diagnosis unit 115 determines that there is no abnormality.
  • the abnormality diagnosis unit 115 outputs the determination result to the notification unit 116.
  • the power data is abnormal enough to be erroneously estimated by the operating state estimation unit 113, and there is a difference from the power data at the time when the feature amount is collected. Is. In such a case, it is considered that the electric device 200 has an abnormality.
  • the notification unit 116 notifies the determination result of the abnormality diagnosis unit 115.
  • the notification unit 116 may notify only when there is an abnormality.
  • the notification may be displayed on a display connected to an input / output unit (not shown) of the abnormality diagnosis device 100.
  • the notification may be a notification to the administrator of the electric device 200 via e-mail or a messaging service, or may be a notification to the existing equipment management system.
  • FIG. 4 is a flowchart of the abnormality diagnosis process according to the first embodiment.
  • the abnormality diagnosis process is executed at a predetermined timing, for example, periodically.
  • the power data collection unit 111 acquires power data from the measuring device 300.
  • the feature amount extraction unit 112 extracts the feature amount from the power data.
  • step S13 the operating state estimation unit 113 estimates the operating state of the electric device 200 from the feature amount with reference to the operating state feature amount database 140.
  • step S14 the setting information collecting unit 114 acquires setting information from the electric device 200.
  • step S15 the abnormality diagnosis unit 115 compares the operating state estimated by the operating state estimation unit 113 in step S13 with the setting information collected by the setting information collecting unit 114 in step S14. In other words, the abnormality diagnosis unit 115 determines whether or not the estimated operating state corresponds to the collected setting information.
  • step S16 the abnormality diagnosis unit 115 ends the abnormality diagnosis process if the comparison results in step S15 match (step S16 ⁇ YES), and proceeds to step S17 if they do not match (step S16 ⁇ NO).
  • step S17 the notification unit 116 notifies the abnormality of the electric device 200.
  • the abnormality diagnosis device 100 detects and notifies an abnormality of the electric device with reference to the feature amount.
  • the feature amount is data collected when the electric device 200 is operating normally (see the operating state feature amount database 140 shown in FIG. 3). Specifically, the abnormality diagnosis device 100 determines that the electric device is abnormal if the estimated operating state and the collected setting information do not correspond to each other.
  • the abnormality diagnosis device 100 can perform abnormality diagnosis without a feature amount at the time of abnormality, which is difficult to acquire and collect, and can be realized at low cost. Further, the abnormality diagnosis device 100 can detect the abnormal state even if there is no feature amount that cannot be collected or a feature amount when the abnormal state is unexpected, and the detection accuracy is high.
  • the feature amount (see the operating state feature amount database 140 shown in FIG. 3) is collected in advance from the electric device 200 or the same type of electric device. Instead of this, it may be acquired during abnormality diagnosis (during monitoring of electrical equipment). By eliminating the need to collect features in advance, it is possible to reduce the labor and cost of collecting them. In addition, the introduction period of the abnormality diagnosis device can be shortened.
  • FIG. 5 is a functional block diagram of the abnormality diagnosis device 100A according to the second embodiment.
  • the feature amount setting unit 117 is added to the control unit 110.
  • the program 131A stored in the storage unit 130 describes the procedure of the feature amount setting process (see FIG. 6 described later).
  • the feature amount setting unit 117 acquires the setting information of the electric device 200 from the setting information collecting unit 114, and whether or not the record of the operating state 141 corresponding to the setting information is stored in the operating state feature amount database 140. Is determined. When not stored, the feature amount setting unit 117 stores a record having the feature amount of the power data extracted by the feature amount extraction unit 112 and the operating state 141 corresponding to the setting information in the operating state feature amount database 140. to add.
  • FIG. 6 is a flowchart of the feature amount setting process according to the second embodiment.
  • the feature amount setting process is executed at a predetermined timing, for example, periodically.
  • the feature amount setting process may be executed at the timing when the setting information collecting unit 114 acquires the setting information. Further, the feature amount setting process may be executed in a short cycle at the beginning of the introduction of the abnormality diagnosis device 100A, and may be executed in a long cycle or at the timing of acquiring the setting information after a predetermined period after the introduction. good.
  • step S21 the feature amount setting unit 117 acquires the setting information of the electric device 200 from the setting information collecting unit 114.
  • the feature amount setting unit 117 determines whether or not the record of the operating state 141 corresponding to the setting information acquired in step S21 is stored in the operating state feature amount database 140 (see FIG. 3). The feature amount setting unit 117 ends the feature amount setting process if it is stored (step S22 ⁇ YES), and proceeds to step S23 if it is not stored (step S22 ⁇ NO).
  • step S23 the power data collection unit 111 acquires power data from the measuring device 300.
  • step S24 the feature amount extraction unit 112 extracts the feature amount from the power data.
  • step S25 the feature amount setting unit 117 adds a new record to the operating state feature amount database 140.
  • the feature amount setting unit 117 sets the operating state 141 of the record to the operating state corresponding to the setting information acquired in step S21. Further, the feature amount setting unit 117 sets the average value 142, the time series value 143, and the frequency distribution 144 of the record as the average value, the time series value, and the frequency distribution extracted in step S24.
  • Second Embodiment Features of the abnormality diagnosis device >> If the operating state corresponding to the acquired setting information is not included in the feature amount, the abnormality diagnosis device 100A determines that it is a new operating state, and transfers the feature amount of the power data to the operating state feature amount database 140. Add. By doing so, the abnormality diagnosis device 100A does not need to collect the feature amount in advance, and the labor and cost for collecting the feature amount can be reduced. In addition, the introduction period of the abnormality diagnosis device 100A can be shortened.
  • FIG. 7 is an overall configuration diagram of the abnormality diagnosis system 10B according to the third embodiment.
  • the abnormality diagnosis system 10B includes a measuring device 300 and an abnormality diagnosis device 100B.
  • the abnormality diagnosis device 100B detects an abnormality in a plurality of electric devices 211 to 213.
  • a plurality of electric devices 211 to 213 are collectively referred to as electric devices 210.
  • FIG. 8 is a functional block diagram of the abnormality diagnosis device 100B according to the third embodiment. Compared with the abnormality diagnosis device 100 (see FIG. 2) in the first embodiment, the operation state estimation unit 113B, the setting information collection unit 114B, the abnormality diagnosis unit 115B, and the operation state feature amount database 140B are different. The differences will be described below.
  • FIG. 9 is a data configuration diagram of the operating state feature amount database 140B according to the third embodiment.
  • the operating state 141B is a combination of the operating states of the electric devices 211 to 213.
  • the third record "cooling, OFF, " Indicates that the operating state (setting state) of the electric device 211, which is an air conditioner, is "cooling" and the electric device 212 is "OFF”. ing.
  • the operating state estimation unit 113B estimates the operating state of each electric device 210 with reference to the operating state feature amount database 140B. Specifically, among the records of the operating state feature amount database 140B, the record having the feature amount most similar to the feature amount extracted by the feature amount extraction unit 112 is searched. The operating state estimation unit 113B outputs the operating state 141B of the search result record to the abnormality diagnosis unit 115B as the estimation result of the operating state of each of the electric devices 210. The setting information collecting unit 114B acquires the operating state of each of the electric devices 210.
  • the abnormality diagnosis unit 115B individually compares the estimated operating state of each electric device 210 with the setting information of each electric device 210 acquired by the setting information collecting unit 114, and if there is a difference, the electric device 210 is concerned. Judge that the electrical equipment is abnormal.
  • FIG. 10 is a flowchart of the abnormality diagnosis process according to the third embodiment.
  • the abnormality diagnosis process is executed at a predetermined timing, for example, periodically.
  • the power data collection unit 111 acquires power data from the measuring device 300.
  • the feature amount extraction unit 112 extracts the feature amount from the power data.
  • step S33 the operating state estimation unit 113B estimates the operating state of each of the electric devices 210 from the feature amount with reference to the operating state feature amount database 140B.
  • step S34 the setting information collecting unit 114B acquires setting information from each of the electric devices 210.
  • step S35 the abnormality diagnosis unit 115B compares the estimation result estimated by the operating state estimation unit 113B in step S33 with the setting information collected by the setting information collecting unit 114B in step S34 for each electric device 210.
  • step S36 the abnormality diagnosis unit 115B ends the abnormality diagnosis process if the comparison results match for all the electrical devices 210 in step S35 (step S36 ⁇ YES), and if there is a mismatched electrical device 210 (step S36 ⁇ NO). ) Proceed to step S37.
  • step S37 the notification unit 116 notifies the abnormality of the electric device 210 in which the estimated operating state and the acquired setting information do not match.
  • the abnormality diagnosis device 100B detects and notifies an abnormality of a plurality of electric devices by referring to the feature amount.
  • the feature amount is data collected when the electric device 210 is operating normally (see the operating state feature amount database 140B shown in FIG. 9).
  • the abnormality diagnosis device 100B can perform abnormality diagnosis without a feature amount at the time of abnormality, which is difficult to acquire and collect, and can be realized at low cost. Further, the abnormality diagnosis device 100B can detect the abnormal state even if there is no feature amount that cannot be collected or a feature amount when the abnormal state is unexpected, and the detection accuracy is high.
  • the feature amount (see the operating state feature amount database 140B shown in FIG. 9) is collected in advance from the electric device 210 or the same type of electric device. Instead of this, it may be acquired during abnormality diagnosis (during monitoring of electrical equipment). By eliminating the need to collect features in advance, it is possible to reduce the labor and cost of collecting them. In addition, the introduction period of the abnormality diagnosis device can be shortened.
  • FIG. 11 is a functional block diagram of the abnormality diagnosis device 100C according to the fourth embodiment.
  • the feature amount setting unit 117C is added to the control unit 110.
  • the program 131C stored in the storage unit 130 describes the procedure of the feature amount setting process (see FIG. 12 described later).
  • the feature amount setting unit 117C acquires the setting information of the electric device 210 from the setting information collecting unit 114B, and determines whether or not the record of the operating state 141B corresponding to the setting information is stored in the operating state feature amount database 140B. judge. If it is not stored, a record having the feature amount (average value, time series value, frequency distribution) of the power data extracted by the feature amount extraction unit 112 and the operating state 141B corresponding to the setting information is stored as the operating state feature amount. Add to database 140B.
  • FIG. 12 is a flowchart of the feature amount setting process according to the fourth embodiment.
  • the feature amount setting process is processed at a predetermined timing, for example, periodically.
  • the feature amount setting process may be executed at the timing when the setting information collecting unit 114B acquires the setting information.
  • the feature amount setting unit 117C acquires the setting information of each electric device 210 from the setting information collecting unit 114B.
  • step S42 the feature amount setting unit 117C determines whether or not the record of the operating state 141B corresponding to the setting information acquired in step S41 is stored in the operating state feature amount database 140B (see FIG. 9).
  • the feature amount setting unit 117C ends the feature amount setting process if it is stored (step S42 ⁇ YES), and proceeds to step S43 if it is not stored (step S42 ⁇ NO).
  • step S43 the power data collection unit 111 acquires power data from the measuring device 300.
  • step S44 the feature amount extraction unit 112 extracts the feature amount from the power data.
  • step S45 the feature amount setting unit 117C adds a new record to the operating state feature amount database 140B.
  • the feature amount setting unit 117C sets the operating state 141B of the record to the operating state corresponding to the setting information of each electric device 210 acquired in step S41. Further, the feature amount setting unit 117C sets the average value 142, the time series value 143, and the frequency distribution 144 of the record as the average value, the time series value, and the frequency distribution extracted in step S44.
  • the abnormality diagnosis device 100C determines that it is a new operating state (combination of operating states). , The feature amount of the power data is added to the operating state feature amount database 140B. By doing so, the abnormality diagnosis device 100C does not need to collect the feature amount in advance, and the labor and cost for collecting the feature amount can be reduced. In addition, the introduction period of the abnormality diagnosis device 100C can be shortened.
  • the abnormality diagnosis device 100 notifies that the electric device 200 is in an abnormal state.
  • the notification includes the abnormality type (cause of the abnormality).
  • FIG. 13 is a functional block diagram of the abnormality diagnosis device 100D according to the fifth embodiment.
  • the storage unit 130 stores the abnormality state feature amount database 150D (see FIG. 14 described later), and the notification unit 116D of the control unit 110. Is different.
  • the program 131D stored in the storage unit 130 describes the procedure for the abnormality diagnosis process (see FIG. 15 described later).
  • FIG. 14 is a data structure diagram of the abnormal state feature amount database 150D according to the fifth embodiment.
  • the abnormal state feature amount database 150D has a data structure in which the operating state 141 of the operating state feature amount database 140 (see FIG. 3) is replaced with the abnormal state 151.
  • the abnormal state 151 indicates the type of the abnormal state of the electric device 200 or the cause of the abnormality.
  • the notification unit 116D searches for a record having a feature amount most similar to the feature amount extracted by the feature amount extraction unit 112 among the records of the abnormal state feature amount database 150D.
  • the notification unit 116D notifies the abnormal state 151 of the search result record as an estimation result of the cause of the abnormal state in the electric device 200.
  • the notification unit 116D outputs, for example, the abnormal state 151 of the record having the highest correlation between the time series value and the frequency distribution as the estimation result.
  • FIG. 15 is a flowchart of the abnormality diagnosis process according to the fifth embodiment.
  • the abnormality diagnosis process is executed at a predetermined timing, for example, periodically.
  • Steps S51 to S56 are the same as steps S11 to S16 (see FIG. 4), respectively.
  • step S57 the notification unit 116D estimates the cause of the abnormality of the electric device 200.
  • step S58 the notification unit 116D notifies the abnormality of the electric device 200 including the cause of the abnormality estimated in step S57.
  • the abnormality diagnosis device 100D notifies the electric device 200 determined to be abnormal including the cause of the abnormality. By knowing the cause of the abnormality, the manager of the electric device 200 can efficiently deal with the abnormality of the electric device 200.
  • the notification unit 116D estimates the cause of the abnormality, but the abnormality diagnosis unit 115 may estimate the cause.
  • the average value, time series value, and frequency distribution of power data are used as the feature quantities of the abnormal state. Among them, it is desirable to estimate by looking at the frequency distribution, especially the high frequency distribution (correlation).
  • the operating state estimation units 113 and 113B search for the record of the feature amount closest to the feature amount of the power data in the operating state feature amount databases 140 and 140B (see FIGS. 3 and 9). ..
  • the operating state estimation units 113 and 113B use the operating states 141 and 141B of the record as the estimation result. Instead of such processing, machine learning techniques may be used.
  • FIG. 16 is a functional block diagram of the abnormality diagnosis device 100E according to the modified example of the first embodiment.
  • the learning unit 117E is added to the control unit 110, and the operating state estimation unit 113E is different. Further, the operating state learning model 140E is stored in the storage unit 130 instead of the operating state feature amount database 140.
  • the operating state learning model 140E is a machine learning model trained using teacher data in which the operating states 141 and 141B of the electric devices 200 and 210 are added as correct labels to the feature quantities of the electric power data.
  • a machine learning technique a neural network, SVM (Support Vector Machine), clustering, or the like can be used.
  • the machine learning technique may be used for the estimation of the cause of the abnormality performed by the notification unit 116D (see FIG. 13).
  • the operating state estimation unit 113E estimates the operating states of the electric devices 200 and 210 from the feature amount of the electric power data with reference to the operating state learning model 140E.
  • the setting information collecting unit 114 acquires the setting information (operating state) that is not in the estimation result of the operating state learning model 140E
  • the learning unit 117E determines that the setting information is correct for the feature amount of the power data at the time of acquisition.
  • the operating state learning model 140E is updated by additionally learning (training) the teacher data to be used.
  • the number of records in the operating state feature amount database 140B is the product of the number of operating states in each electric device 210. It increases exponentially as the number of devices 210 increases.
  • the shift number and the value of the expansion coefficient having the maximum value after the wavelet transform of the electric power data may be registered (stored) as a feature amount.
  • the operating state of each electric device 210 can be estimated by obtaining the expansion coefficient of the power data after wavelet transform and taking the correspondence between the number of shifts and the value in the feature data.
  • the number of feature amounts (the number of records in the operating state feature amount database 140B) is the sum of the number of operating states in each electric device 210, and is proportional to the number of electric devices 210.
  • the number of registered features can be reduced, which in turn improves the speed of estimation processing.
  • the cost of collecting features can be reduced.
  • the notification unit 116D (see FIG. 13) may estimate the abnormal state (cause of the abnormality) of the electric device.
  • the operating state feature amount database 140 may include only a part of the average value 142, the time series value 143, and the frequency distribution 144 of the power data.
  • the operating state estimation units 113 and 113B estimate the operating state by comparing some of the features.
  • the programs 131, 131A, 131B, 131C, 131D, 131E are stored in the abnormality diagnosis devices 100, 100A, 100B, 100C, 100D, 100E, which are computers.
  • the programs 131, 131A, 131B, 131C, 131D, 131E in the recording medium 910 may be read and loaded into the storage unit 130 and executed, or may be installed and executed from the recording medium 910.
  • FIG. 17 is a diagram showing a recording medium 910 according to the above-described embodiment. By installing the recording medium 910 on the computer 900, the computer can function as the abnormality diagnostic devices 100, 100A, 100B, 100C, 100D, 100E.
  • the average value, the time series value, the frequency distribution (including the expansion coefficient after the wavelet transform) and the like are given as the feature amount of the power data, but other feature amounts such as the instantaneous maximum value are used. You may.
  • the feature amount (operating state feature amount database) is stored in the storage unit of the abnormality diagnosis device, but the present invention is not limited to this.
  • the operating state estimation unit may estimate the operating state of the electric device by referring to the feature amount stored in the outside.
  • the present invention can take various other embodiments, and further, various changes such as omission and replacement can be made without departing from the gist of the present invention.
  • These embodiments and variations thereof are included in the scope and gist of the invention described in the present specification and the like, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.
  • Abnormality diagnosis system 100, 100A, 100B, 100C, 100D, 100E Abnormality diagnosis device 111 Power data collection unit 112 Feature quantity extraction unit 113, 113B, 113E Operation status estimation unit 114, 114B Setting information collection unit 115, 115B Abnormality Diagnosis unit 116, 116D Notification unit 117, 117C Feature amount setting unit 117E Learning unit 140, 140B Operating state feature amount database (operating state feature amount data) 140E Operating state learning model (machine learning model) 150D Abnormal state feature database (fault feature data) 200, 210, 211,212,213 Electrical equipment

Abstract

The present invention addresses the problem of making it possible to diagnose an abnormality even when there is no data from when an electric device abnormality occurs. An abnormality diagnosis device (100) is equipped with: a power data collection unit (111) for collecting power data about an electric device; a feature extraction unit (112) for acquiring a feature from the collected power data; an operating state estimation unit (113) for estimating the operating state of the electric device from operating state/feature data (operating state/feature database (140)), which includes the relationship between electric device operating state and power data features during said operating state, and also from said acquired feature; a setting information collection unit (114) for collecting the setting information for setting the operating state of the electric device; and an abnormality diagnosis unit (115) for determining that the electric device is exhibiting an abnormality when the estimated operating state and the collected setting information do not correspond with one another.

Description

異常診断装置、プログラムおよび異常診断方法Abnormality diagnosis device, program and abnormality diagnosis method
 本発明は、電気機器の異常診断装置、プログラムおよび異常診断方法に関する。 The present invention relates to an abnormality diagnosis device, a program, and an abnormality diagnosis method for electrical equipment.
 電気機器に供給される電流や電圧情報から電気機器の稼働状態を推定する技術があり、一般に電力ディスアグリゲーション、用途分解、非侵入型負荷モニタリング(NILM: Non-Intrusive Load Monitoring)などと呼ばれている。 There is a technology to estimate the operating state of electrical equipment from the current and voltage information supplied to the electrical equipment, and it is generally called power disaggregation, application decomposition, non-intrusive load monitoring (NILM: Non-Intrusive Load Monitoring), etc. There is.
 特許文献1には、電力需要家の給電線引込口付近に設置した測定センサーと、前記測定センサーで検出した測定データから基本波並びに高調波の電流とそれらの電圧に対する位相に関するデータを取り出すデータ抽出手段と、前記データ抽出手段からの基本波並びに高調波の電流とそれらの電圧に対する位相に関するデータを基に、当該電力需要家が使用している電気機器の動作状態を推定するパターン認識手段とを備えたことを特徴とする電気機器モニタリングシステムが記載されている。 In Patent Document 1, a measurement sensor installed near the power supply line inlet of a power consumer and data extraction for extracting data on the currents of fundamental waves and harmonics and their phases with respect to their voltages from the measurement data detected by the measurement sensors. A means and a pattern recognition means for estimating the operating state of the electric equipment used by the electric power consumer based on the data on the currents of the fundamental waves and harmonics from the data extraction means and the phase with respect to their voltages. An electrical equipment monitoring system characterized by being equipped is described.
 また、特許文献2には、1又は複数の機器の状態を検出する機器状態検出装置であって、機器が設置されている環境の物理量を計測する計測手段と、前記計測手段が計測した計測値の特徴量を計算する特徴量計算手段と、予め前記機器ごとの前記特徴量とこれに対応する機器状態とを辞書データとして記憶する記憶手段と、前記特徴量計算手段が計算した特徴量を検索キーとして前記辞書データに記憶された特徴量を検索し、検索結果として特定した該特徴量に対応する機器状態に基づいて機器状態を検出する機器状態検出手段とを有することを特徴とする機器状態検出装置が記載されている。 Further, Patent Document 2 describes a device state detecting device for detecting the state of one or a plurality of devices, which is a measuring means for measuring a physical quantity of an environment in which the device is installed, and a measured value measured by the measuring means. A feature amount calculation means for calculating the feature amount of the above, a storage means for storing the feature amount for each device and the corresponding device state as dictionary data in advance, and a feature amount calculated by the feature amount calculation means are searched. The device state is characterized by having a device state detecting means for searching the feature amount stored in the dictionary data as a key and detecting the device state based on the device state corresponding to the feature amount specified as the search result. The detector is described.
特開2000-292465号公報Japanese Unexamined Patent Publication No. 2000-292465 国際公開第2009/125659号International Publication No. 2009/1256559
 特許文献1および特許文献2に記載の手法では、動作状態を推定する(モニタリングする)ために、電気機器の動作状態を教師データとしてあらかじめ与えて学習しておく必要がある。対象とする電気機器の異常を判定する場合は、その異常状態にある電気機器の動作状態のデータが必要となるが、電気機器の異常状態のデータを取得することは困難なケースが多い。
 本発明は、このような背景を鑑みてなされたものであり、電気機器の異常時のデータがない場合にも、異常の診断を可能とする異常診断装置、プログラムおよび異常診断方法を提供することを課題とする。
In the methods described in Patent Document 1 and Patent Document 2, in order to estimate (monitor) the operating state, it is necessary to give and learn the operating state of the electric device as teacher data in advance. When determining an abnormality in the target electric device, data on the operating state of the electric device in the abnormal state is required, but it is often difficult to acquire data on the abnormal state of the electric device.
The present invention has been made in view of such a background, and provides an abnormality diagnosis device, a program, and an abnormality diagnosis method capable of diagnosing an abnormality even when there is no data at the time of abnormality of an electric device. Is the subject.
 上記した課題を解決するため、本発明に係る異常診断装置は、電気機器の電力データを収集する電力データ収集部と、収集された前記電力データから特徴量を取得する特徴量抽出部と、前記電気機器の稼働状態と当該稼働状態における電力データの特徴量との関係を含む稼働状態特徴量データ、および取得された特徴量から前記電気機器の稼働状態を推定する稼働状態推定部と、前記電気機器の稼働状態を設定する設定情報を収集する設定情報収集部と、推定された稼働状態と、収集された設定情報とが対応しないならば、前記電気機器が異常と判定する異常診断部と、を備える。 In order to solve the above-mentioned problems, the abnormality diagnosis device according to the present invention includes a power data collection unit that collects power data of an electric device, a feature amount extraction unit that acquires a feature amount from the collected power data, and the above-mentioned. The operating state feature amount data including the relationship between the operating state of the electric device and the feature amount of the power data in the operating state, the operating state estimation unit that estimates the operating state of the electric device from the acquired feature amount, and the electricity. A setting information collection unit that collects setting information that sets the operating state of the device, an abnormality diagnosis unit that determines that the electrical device is abnormal if the estimated operating state does not correspond to the collected setting information, To prepare for.
 本発明によれば、電気機器の異常時のデータがない場合にも、異常の診断を可能とする異常診断装置、プログラムおよび異常診断方法を提供することができる。上記した以外の課題、構成および効果は、以下の実施形態の説明により明らかにされる。 According to the present invention, it is possible to provide an abnormality diagnosis device, a program, and an abnormality diagnosis method capable of diagnosing an abnormality even when there is no data at the time of abnormality of an electric device. Issues, configurations and effects other than those described above will be clarified by the description of the following embodiments.
第1の実施形態に係る異常診断システムの全体構成図である。It is an overall block diagram of the abnormality diagnosis system which concerns on 1st Embodiment. 第1の実施形態に係る異常診断装置の機能ブロック図である。It is a functional block diagram of the abnormality diagnosis apparatus which concerns on 1st Embodiment. 第1の実施形態に係る稼働状態特徴量データベースのデータ構成図である。It is a data structure diagram of the operating state feature amount database which concerns on 1st Embodiment. 第1の実施形態に係る異常診断処理のフローチャートである。It is a flowchart of abnormality diagnosis processing which concerns on 1st Embodiment. 第2の実施形態に係る異常診断装置の機能ブロック図である。It is a functional block diagram of the abnormality diagnosis apparatus which concerns on 2nd Embodiment. 第2の実施形態に係る特徴量設定処理のフローチャートである。It is a flowchart of the feature amount setting process which concerns on 2nd Embodiment. 第3の実施形態に係る異常診断システムの全体構成図である。It is an overall block diagram of the abnormality diagnosis system which concerns on 3rd Embodiment. 第3の実施形態に係る異常診断装置の機能ブロック図である。It is a functional block diagram of the abnormality diagnosis apparatus which concerns on 3rd Embodiment. 第3の実施形態に係る稼働状態特徴量データベースのデータ構成図である。It is a data structure diagram of the operating state feature amount database which concerns on 3rd Embodiment. 第3の実施形態に係る異常診断処理のフローチャートである。It is a flowchart of abnormality diagnosis processing which concerns on 3rd Embodiment. 第4の実施形態に係る異常診断装置の機能ブロック図である。It is a functional block diagram of the abnormality diagnosis apparatus which concerns on 4th Embodiment. 第4の実施形態に係る特徴量設定処理のフローチャートである。It is a flowchart of the feature amount setting process which concerns on 4th Embodiment. 第5の実施形態に係る異常診断装置の機能ブロック図である。It is a functional block diagram of the abnormality diagnosis apparatus which concerns on 5th Embodiment. 第5の実施形態に係る異常状態特徴量データベースのデータ構成図である。It is a data structure diagram of the abnormal state feature amount database which concerns on 5th Embodiment. 第5の実施形態に係る異常診断処理のフローチャートである。It is a flowchart of abnormality diagnosis processing which concerns on 5th Embodiment. 第1の実施形態の変形例に係る異常診断装置の機能ブロック図である。It is a functional block diagram of the abnormality diagnosis apparatus which concerns on the modification of 1st Embodiment. 上記した実施形態に係る記録媒体を示す図である。It is a figure which shows the recording medium which concerns on the said embodiment.
≪異常診断装置の概要≫
 以下に、本発明を実施するための形態(実施形態)における異常診断装置を説明する。異常診断装置は、電気機器が使用する電力データと、電気機器の設定情報とを取得する。設定情報とは、電気機器に対して各種稼働状態を設定する情報であって、例えば、エアコンでは、停止、冷房、暖房、除湿、温度設定、風向などがある。異常診断装置は、電力データから電気機器の稼働状態を推定して、取得した設定情報と異なる場合には、異常と判定する。
 従来技術で異常状態を推定しようとすると、異常状態にある電気機器の電力データを収集する必要がある。異常状態は1つに限らず、想定外を含め全ての異常状態にある電気機器の電力データを収集するのは困難である。
 従来技術に対して、本発明の実施形態における電力データから推定される稼働状態は、電気機器の通常の稼働状態である。このため、推定に必要なデータ(稼働状態を特徴づける特徴量)を集めることは、容易であり、低コストで電気機器の異常診断が可能となる。
≪Overview of abnormality diagnosis device≫
Hereinafter, the abnormality diagnosis device in the embodiment (embodiment) for carrying out the present invention will be described. The abnormality diagnosis device acquires the power data used by the electric device and the setting information of the electric device. The setting information is information for setting various operating states for an electric device, and for example, in an air conditioner, there are stop, cooling, heating, dehumidification, temperature setting, wind direction, and the like. The abnormality diagnosis device estimates the operating state of the electric device from the power data, and if it is different from the acquired setting information, determines that it is an abnormality.
In order to estimate the abnormal state by the conventional technique, it is necessary to collect the power data of the electric device in the abnormal state. The number of abnormal states is not limited to one, and it is difficult to collect power data of all electric devices in abnormal states including unexpected ones.
With respect to the prior art, the operating state estimated from the power data in the embodiment of the present invention is the normal operating state of the electrical equipment. Therefore, it is easy to collect the data necessary for estimation (features that characterize the operating state), and it is possible to diagnose abnormalities in electrical equipment at low cost.
≪第1の実施形態:異常診断システムの全体構成≫
 図1は、第1の実施形態に係る異常診断システム10の全体構成図である。異常診断システム10は、計測装置300と異常診断装置100(後記する図2参照)とを含んで構成される。計測装置300は、電源400から電気機器200に供給される電力を計測し、計測した電力データをネットワーク500経由で異常診断装置100に送信する。計測装置300は、計測データを有する装置やシステム、例えば、エネルギー管理システムであってもよい。また、異常診断装置100は、ネットワーク500を介して電気機器200から設定情報を取得する。
<< First Embodiment: Overall Configuration of Abnormality Diagnosis System >>
FIG. 1 is an overall configuration diagram of the abnormality diagnosis system 10 according to the first embodiment. The abnormality diagnosis system 10 includes a measuring device 300 and an abnormality diagnosis device 100 (see FIG. 2 described later). The measuring device 300 measures the electric power supplied from the power source 400 to the electric device 200, and transmits the measured electric power data to the abnormality diagnosis device 100 via the network 500. The measuring device 300 may be a device or system having measurement data, for example, an energy management system. Further, the abnormality diagnosis device 100 acquires setting information from the electric device 200 via the network 500.
 電気機器200は、電力を用いて動作する機器であり、例えば、エアコン、冷蔵庫、照明器具、電気給湯器などである。電源400は、電力供給源であり、電力系統や構内配電網などの電力線を経由して電気機器200に電力を供給する。ここで、電力データとは、電力に関わるデータであり、消費電力量(WH)、消費電力(W)、電圧(V)、電流(A)、力率などの瞬間値および時系列値などである。
 ネットワーク500は、電力データや設定情報の伝送に用いられる。ネットワーク500は、有線、無線を問わず、公衆通信網、構内通信網などの通信網を利用してもよい。
The electric device 200 is a device that operates by using electric power, and is, for example, an air conditioner, a refrigerator, a lighting fixture, an electric water heater, and the like. The power source 400 is a power supply source, and supplies power to the electric device 200 via a power line such as a power system or a premises distribution network. Here, the electric power data is data related to electric power, and is an instantaneous value such as electric energy (WH), electric power consumption (W), voltage (V), current (A), power factor, and time series value. be.
The network 500 is used for transmitting power data and setting information. The network 500 may use a communication network such as a public communication network or a private communication network regardless of whether it is wired or wireless.
≪第1の実施形態:異常診断装置の構成≫
 図2は、第1の実施形態に係る異常診断装置100の機能ブロック図である。異常診断装置100は、制御部110、記憶部130、および通信部180を備える。通信部180は、NIC(Network Interface Card)などの通信機器から構成され、電気機器200や計測装置300を含む他の装置と通信データを送受信する。
 記憶部130は、ROM(Read Only Memory)やRAM(Random Access Memory)、フラッシュメモリなどの記憶機器から構成される。記憶部130には、稼働状態特徴量データベース140(図2では稼働状態特徴量DB(database)と記載、後記する図3参照)やプログラム131が記憶される。プログラム131には、異常診断処理(後記する図4参照)の手順が記述される。
<< First Embodiment: Configuration of Abnormality Diagnosis Device >>
FIG. 2 is a functional block diagram of the abnormality diagnosis device 100 according to the first embodiment. The abnormality diagnosis device 100 includes a control unit 110, a storage unit 130, and a communication unit 180. The communication unit 180 is composed of a communication device such as a NIC (Network Interface Card), and transmits / receives communication data to / from other devices including the electric device 200 and the measuring device 300.
The storage unit 130 is composed of a storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), and a flash memory. The storage unit 130 stores the operating state feature amount database 140 (described as the operating state feature amount DB (database) in FIG. 2, see FIG. 3 described later) and the program 131. The program 131 describes the procedure for the abnormality diagnosis process (see FIG. 4 described later).
≪第1の実施形態:異常診断装置の構成:稼働状態特徴量データベース≫
 図3は、第1の実施形態に係る稼働状態特徴量データベース140のデータ構成図である。稼働状態特徴量データベース140は、電気機器200の稼働状態と、当該稼働状態における電力データの特徴量とを関連付けて格納している。稼働状態特徴量データベース140は、表形式のデータであって、1つの行(レコード)は、稼働状態141、平均値142、時系列値143、および周波数分布144の列(属性)を含む。
<< First embodiment: Configuration of abnormality diagnosis device: Operation state feature amount database >>
FIG. 3 is a data structure diagram of the operating state feature amount database 140 according to the first embodiment. The operating state feature amount database 140 stores the operating state of the electric device 200 and the feature amount of the electric power data in the operating state in association with each other. The operating state feature amount database 140 is tabular data, and one row (record) includes an operating state 141, an average value 142, a time series value 143, and a column (attribute) of a frequency distribution 144.
 稼働状態141は、電気機器200の稼働状態であり、例えば、「OFF」(停止)、「除湿」、「冷房」などの状態(設定状態、運転状態)がある。平均値142と時系列値143とは、電気機器200が稼働状態141にあるときの電力データの平均と時系列値(時系列データ)である。周波数分布144は、例えば、1~2周期(50Hzの商用電源なら20~40ms)の電力データをウェーブレット変換ないしは離散フーリエ変換して得られた周波数分布である。ここで、電力データとは、前記したように消費電力量、消費電力、電圧、電流、力率などである。 The operating state 141 is an operating state of the electric device 200, and there are states (set state, operating state) such as "OFF" (stop), "dehumidification", and "cooling". The average value 142 and the time series value 143 are the average and time series value (time series data) of the electric power data when the electric device 200 is in the operating state 141. The frequency distribution 144 is, for example, a frequency distribution obtained by wavelet transforming or discrete Fourier transforming power data having one or two cycles (20 to 40 ms for a commercial power supply of 50 Hz). Here, the power data is, as described above, power consumption, power consumption, voltage, current, power factor, and the like.
 特徴量は、事前に電気機器200から取得してもよいし、同種の電気機器のデータを用いてもよい。同種の電気機器とは、例えば、エアコン、冷蔵庫などの機種でもよいし、エアコンのなかでも同程度の消費電力のものでもよいし、同じ型番のエアコンでもよい。可能な限り、診断対象である電気機器200と似た特徴量となるものを用いることが望ましい。
 特徴量を取得するには、ネットワーク500(図1参照)に接続される、特徴量を格納したサーバ(不図示)から取得してもよい。または、異常診断装置100の入出力部(不図示)に接続された記録媒体にある特徴量をコピーして取得してもよい。
The feature amount may be acquired from the electric device 200 in advance, or data of the same type of electric device may be used. The electric device of the same type may be, for example, a model such as an air conditioner or a refrigerator, an air conditioner having the same power consumption, or an air conditioner having the same model number. As much as possible, it is desirable to use a device having a feature amount similar to that of the electrical device 200 to be diagnosed.
In order to acquire the feature amount, it may be acquired from a server (not shown) connected to the network 500 (see FIG. 1) and storing the feature amount. Alternatively, the feature amount on the recording medium connected to the input / output unit (not shown) of the abnormality diagnosis device 100 may be copied and acquired.
≪第1の実施形態:異常診断装置の構成:制御部≫
 図2に戻って、制御部110は、電力データ収集部111、特徴量抽出部112、稼働状態推定部113、設定情報収集部114、異常診断部115、および通知部116を備える。
 電力データ収集部111は、計測装置300(図1参照)から電力データを取得して、特徴量抽出部112に出力する。特徴量抽出部112は、電力データから特徴量として、例えば、平均値や時系列値、周波数分布を抽出する。
<< First Embodiment: Configuration of Abnormality Diagnosis Device: Control Unit >>
Returning to FIG. 2, the control unit 110 includes a power data collection unit 111, a feature amount extraction unit 112, an operating state estimation unit 113, a setting information collection unit 114, an abnormality diagnosis unit 115, and a notification unit 116.
The power data collection unit 111 acquires power data from the measuring device 300 (see FIG. 1) and outputs the power data to the feature amount extraction unit 112. The feature amount extraction unit 112 extracts, for example, an average value, a time series value, and a frequency distribution as feature amounts from the power data.
 稼働状態推定部113は、稼働状態特徴量データベース140(図3参照)のレコードのなかで、特徴量抽出部112が抽出した特徴量に最も類似する特徴量をもつレコードを検索する。稼働状態推定部113は、検索結果のレコードの稼働状態141を、電気機器200の稼働状態の推定結果として異常診断部115に出力する。稼働状態推定部113は、例えば、時系列値や周波数分布の相関が最も高いレコードの稼働状態141を推定結果として出力する。 The operating state estimation unit 113 searches for a record having a feature amount most similar to the feature amount extracted by the feature amount extracting unit 112 among the records of the operating state feature amount database 140 (see FIG. 3). The operating state estimation unit 113 outputs the operating state 141 of the search result record to the abnormality diagnosis unit 115 as the estimation result of the operating state of the electric device 200. The operating state estimation unit 113 outputs, for example, the operating state 141 of the record having the highest correlation between the time series value and the frequency distribution as the estimation result.
 設定情報収集部114は、電気機器200から稼働状態を設定する設定情報を取得して、異常診断部115に出力する。異常診断部115は、稼働状態推定部113が推定した稼働状態と、設定情報収集部114が取得した設定情報とを比較し、差異があれば電気機器200が異常であると判定する。差異がなければ、異常診断部115は、異常なしと判定する。異常診断部115は、判定した結果を通知部116に出力する。
 稼働状態の推定結果と設定情報とに差異がある場合は、稼働状態推定部113で誤推定するほどに電力データに異常があり、特徴量を収集した時点における電力データとの差があるということである。このような場合には、電気機器200に異常があると考えられる。
The setting information collecting unit 114 acquires the setting information for setting the operating state from the electric device 200 and outputs it to the abnormality diagnosis unit 115. The abnormality diagnosis unit 115 compares the operating state estimated by the operating state estimation unit 113 with the setting information acquired by the setting information collecting unit 114, and if there is a difference, determines that the electric device 200 is abnormal. If there is no difference, the abnormality diagnosis unit 115 determines that there is no abnormality. The abnormality diagnosis unit 115 outputs the determination result to the notification unit 116.
If there is a difference between the estimated result of the operating state and the setting information, it means that the power data is abnormal enough to be erroneously estimated by the operating state estimation unit 113, and there is a difference from the power data at the time when the feature amount is collected. Is. In such a case, it is considered that the electric device 200 has an abnormality.
 通知部116は、異常診断部115の判定結果を通知する。通知部116は、異常がある場合にのみ通知するようにしてもよい。通知は、異常診断装置100の入出力部(不図示)に接続されたディスプレイに表示されてもよい。または、通知は、メールやメッセージングサービスを介しての電気機器200の管理者への通知であってもよいし、既存の設備管理システムへの通知であってもよい。 The notification unit 116 notifies the determination result of the abnormality diagnosis unit 115. The notification unit 116 may notify only when there is an abnormality. The notification may be displayed on a display connected to an input / output unit (not shown) of the abnormality diagnosis device 100. Alternatively, the notification may be a notification to the administrator of the electric device 200 via e-mail or a messaging service, or may be a notification to the existing equipment management system.
≪第1の実施形態:異常診断装置の構成:異常診断処理≫
 図4は、第1の実施形態に係る異常診断処理のフローチャートである。異常診断処理は、所定のタイミング、例えば定期的に実行される。
 ステップS11において電力データ収集部111は、計測装置300から電力データを取得する。
 ステップS12において特徴量抽出部112は、電力データから特徴量を抽出する。
<< First Embodiment: Configuration of Abnormality Diagnosis Device: Abnormality Diagnosis Processing >>
FIG. 4 is a flowchart of the abnormality diagnosis process according to the first embodiment. The abnormality diagnosis process is executed at a predetermined timing, for example, periodically.
In step S11, the power data collection unit 111 acquires power data from the measuring device 300.
In step S12, the feature amount extraction unit 112 extracts the feature amount from the power data.
 ステップS13において稼働状態推定部113は、稼働状態特徴量データベース140を参照して、特徴量から電気機器200の稼働状態を推定する。
 ステップS14において設定情報収集部114は、電気機器200から設定情報を取得する。
 ステップS15において異常診断部115は、ステップS13において稼働状態推定部113が推定した稼働状態と、ステップS14において設定情報収集部114が収集した設定情報とを比較する。換言すれば、異常診断部115は、推定された稼働状態と、収集された設定情報とが対応するか対応しないかを判定する。
In step S13, the operating state estimation unit 113 estimates the operating state of the electric device 200 from the feature amount with reference to the operating state feature amount database 140.
In step S14, the setting information collecting unit 114 acquires setting information from the electric device 200.
In step S15, the abnormality diagnosis unit 115 compares the operating state estimated by the operating state estimation unit 113 in step S13 with the setting information collected by the setting information collecting unit 114 in step S14. In other words, the abnormality diagnosis unit 115 determines whether or not the estimated operating state corresponds to the collected setting information.
 ステップS16において異常診断部115は、ステップS15における比較結果が一致すれば(ステップS16→YES)ならば異常診断処理を終了し、不一致ならば(ステップS16→NO)ならばステップS17に進む。
 ステップS17において通知部116は、電気機器200の異常を通知する。
In step S16, the abnormality diagnosis unit 115 ends the abnormality diagnosis process if the comparison results in step S15 match (step S16 → YES), and proceeds to step S17 if they do not match (step S16 → NO).
In step S17, the notification unit 116 notifies the abnormality of the electric device 200.
≪第1の実施形態:異常診断装置の特徴≫
 異常診断装置100は、特徴量を参照して電気機器の異常を検出し、通知する。特徴量は、電気機器200が正常に稼働しているときに収集されたデータ(図3記載の稼働状態特徴量データベース140参照)である。詳しくは、異常診断装置100は、推定された稼働状態と、収集された設定情報とが対応しないならば、前記電気機器が異常と判定する。
 異常診断装置100は、取得や収集が困難である異常時の特徴量なしに異常診断が可能であり、低コストで実現可能となる。また、異常診断装置100は、収集できない特徴量や予想外の異常状態であるときの特徴量がなくても異常状態を検出でき、検出精度が高い。
<< First Embodiment: Features of the Abnormality Diagnosis Device >>
The abnormality diagnosis device 100 detects and notifies an abnormality of the electric device with reference to the feature amount. The feature amount is data collected when the electric device 200 is operating normally (see the operating state feature amount database 140 shown in FIG. 3). Specifically, the abnormality diagnosis device 100 determines that the electric device is abnormal if the estimated operating state and the collected setting information do not correspond to each other.
The abnormality diagnosis device 100 can perform abnormality diagnosis without a feature amount at the time of abnormality, which is difficult to acquire and collect, and can be realized at low cost. Further, the abnormality diagnosis device 100 can detect the abnormal state even if there is no feature amount that cannot be collected or a feature amount when the abnormal state is unexpected, and the detection accuracy is high.
≪第2の実施形態の概要≫
 第1の実施形態では、特徴量(図3記載の稼働状態特徴量データベース140参照)は、事前に電気機器200、ないしは同種の電気機器から収集している。これに替えて、異常診断中(電気機器の監視中)に取得するようにしてもよい。
 事前の特徴量の収集が不要となることで、収集する手間やコストを削減できる。また、異常診断装置の導入期間を短縮することができる。
<< Outline of the second embodiment >>
In the first embodiment, the feature amount (see the operating state feature amount database 140 shown in FIG. 3) is collected in advance from the electric device 200 or the same type of electric device. Instead of this, it may be acquired during abnormality diagnosis (during monitoring of electrical equipment).
By eliminating the need to collect features in advance, it is possible to reduce the labor and cost of collecting them. In addition, the introduction period of the abnormality diagnosis device can be shortened.
≪第2の実施形態:異常診断装置の構成≫
 図5は、第2の実施形態に係る異常診断装置100Aの機能ブロック図である。第1の実施形態における異常診断装置100(図2参照)と比較して、制御部110に特徴量設定部117が加わる。記憶部130に記憶されるプログラム131Aには、特徴量設定処理(後記する図6参照)の手順が記述される。
<< Second Embodiment: Configuration of Abnormality Diagnosis Device >>
FIG. 5 is a functional block diagram of the abnormality diagnosis device 100A according to the second embodiment. Compared with the abnormality diagnosis device 100 (see FIG. 2) in the first embodiment, the feature amount setting unit 117 is added to the control unit 110. The program 131A stored in the storage unit 130 describes the procedure of the feature amount setting process (see FIG. 6 described later).
 特徴量設定部117は、設定情報収集部114から電気機器200の設定情報を取得して、稼働状態特徴量データベース140に当該設定情報に対応する稼働状態141であるレコードが格納されているか否かを判定する。格納されていない場合には、特徴量設定部117は、特徴量抽出部112が抽出した電力データの特徴量と、設定情報に対応する稼働状態141とをもつレコードを稼働状態特徴量データベース140に追加する。 The feature amount setting unit 117 acquires the setting information of the electric device 200 from the setting information collecting unit 114, and whether or not the record of the operating state 141 corresponding to the setting information is stored in the operating state feature amount database 140. Is determined. When not stored, the feature amount setting unit 117 stores a record having the feature amount of the power data extracted by the feature amount extraction unit 112 and the operating state 141 corresponding to the setting information in the operating state feature amount database 140. to add.
≪第2の実施形態:特徴量設定処理≫
 図6は、第2の実施形態に係る特徴量設定処理のフローチャートである。特徴量設定処理は、所定のタイミング、例えば、定期的に実行される。特徴量設定処理は、設定情報収集部114が設定情報を取得するタイミングで、実行されてもよい。また、特徴量設定処理は、異常診断装置100Aの導入当初は、短い周期で実行され、導入後の所定の期間を過ぎた後は、長い周期、ないしは設定情報を取得するタイミングで実行されてもよい。
<< Second embodiment: Feature amount setting process >>
FIG. 6 is a flowchart of the feature amount setting process according to the second embodiment. The feature amount setting process is executed at a predetermined timing, for example, periodically. The feature amount setting process may be executed at the timing when the setting information collecting unit 114 acquires the setting information. Further, the feature amount setting process may be executed in a short cycle at the beginning of the introduction of the abnormality diagnosis device 100A, and may be executed in a long cycle or at the timing of acquiring the setting information after a predetermined period after the introduction. good.
 ステップS21において特徴量設定部117は、設定情報収集部114から電気機器200の設定情報を取得する。
 ステップS22において特徴量設定部117は、稼働状態特徴量データベース140(図3参照)に、ステップS21において取得した設定情報に対応する稼働状態141であるレコードが格納されているか否かを判定する。特徴量設定部117は、格納済みなら(ステップS22→YES)ならば特徴量設定処理を終了し、未格納なら(ステップS22→NO)ならステップS23に進む。
In step S21, the feature amount setting unit 117 acquires the setting information of the electric device 200 from the setting information collecting unit 114.
In step S22, the feature amount setting unit 117 determines whether or not the record of the operating state 141 corresponding to the setting information acquired in step S21 is stored in the operating state feature amount database 140 (see FIG. 3). The feature amount setting unit 117 ends the feature amount setting process if it is stored (step S22 → YES), and proceeds to step S23 if it is not stored (step S22 → NO).
 ステップS23において電力データ収集部111は、計測装置300から電力データを取得する。
 ステップS24において特徴量抽出部112は、電力データから特徴量を抽出する。
 ステップS25において特徴量設定部117は、稼働状態特徴量データベース140に新しいレコードを追加する。次に、特徴量設定部117は、当該レコードの稼働状態141をステップS21で取得した設定情報に対応する稼働状態とする。また、特徴量設定部117は、当該レコードの平均値142、時系列値143、および周波数分布144をステップS24で抽出された平均値、時系列値、および周波数分布とする。
In step S23, the power data collection unit 111 acquires power data from the measuring device 300.
In step S24, the feature amount extraction unit 112 extracts the feature amount from the power data.
In step S25, the feature amount setting unit 117 adds a new record to the operating state feature amount database 140. Next, the feature amount setting unit 117 sets the operating state 141 of the record to the operating state corresponding to the setting information acquired in step S21. Further, the feature amount setting unit 117 sets the average value 142, the time series value 143, and the frequency distribution 144 of the record as the average value, the time series value, and the frequency distribution extracted in step S24.
≪第2の実施形態:異常診断装置の特徴≫
 異常診断装置100Aは、取得した設定情報に対応する稼働状態が、特徴量に含まれない場合には、新しい稼働状態であると判定して、電力データの特徴量を稼働状態特徴量データベース140に加える。このようにすることで、異常診断装置100Aでは、事前の特徴量の収集が不要となり、特徴量を収集する手間やコストを削減できる。また、異常診断装置100Aの導入期間を短縮することができる。
<< Second Embodiment: Features of the abnormality diagnosis device >>
If the operating state corresponding to the acquired setting information is not included in the feature amount, the abnormality diagnosis device 100A determines that it is a new operating state, and transfers the feature amount of the power data to the operating state feature amount database 140. Add. By doing so, the abnormality diagnosis device 100A does not need to collect the feature amount in advance, and the labor and cost for collecting the feature amount can be reduced. In addition, the introduction period of the abnormality diagnosis device 100A can be shortened.
≪第3の実施形態の概要≫
 上記した実施形態では、電気機器は1つであった。第3の実施形態では、電気機器210(後記する図7参照)は複数であって、個々の電気機器の異常状態を検出する。
 図7は、第3の実施形態に係る異常診断システム10Bの全体構成図である。異常診断システム10Bは、計測装置300および異常診断装置100Bを含む。異常診断装置100Bは、複数の電気機器211~213の異常を検出する。図7では、3つの電気機器211~213であるが、1つでも、2つでも、4つ以上であってもよい。なお、複数の電気機器211~213を総称して電気機器210と記す。
<< Outline of the third embodiment >>
In the above embodiment, there is only one electric device. In the third embodiment, there are a plurality of electric devices 210 (see FIG. 7 described later), and an abnormal state of each electric device is detected.
FIG. 7 is an overall configuration diagram of the abnormality diagnosis system 10B according to the third embodiment. The abnormality diagnosis system 10B includes a measuring device 300 and an abnormality diagnosis device 100B. The abnormality diagnosis device 100B detects an abnormality in a plurality of electric devices 211 to 213. In FIG. 7, there are three electric devices 211 to 213, but one, two, or four or more may be used. In addition, a plurality of electric devices 211 to 213 are collectively referred to as electric devices 210.
≪第3の実施形態:異常診断装置の構成≫
 図8は、第3の実施形態に係る異常診断装置100Bの機能ブロック図である。第1の実施形態における異常診断装置100(図2参照)と比較して、稼働状態推定部113B、設定情報収集部114B、異常診断部115B、および稼働状態特徴量データベース140Bが異なる。以下、異なる点を説明する。
<< Third Embodiment: Configuration of Abnormality Diagnosis Device >>
FIG. 8 is a functional block diagram of the abnormality diagnosis device 100B according to the third embodiment. Compared with the abnormality diagnosis device 100 (see FIG. 2) in the first embodiment, the operation state estimation unit 113B, the setting information collection unit 114B, the abnormality diagnosis unit 115B, and the operation state feature amount database 140B are different. The differences will be described below.
 図9は、第3の実施形態に係る稼働状態特徴量データベース140Bのデータ構成図である。稼働状態141Bは、電気機器211~213個々の稼働状態の組み合わせとなる。例えば、3つ目のレコードの「冷房,OFF,・・・」は、エアコンである電気機器211の稼働状態(設定状態)が「冷房」で、電気機器212は「OFF」であることを示している。 FIG. 9 is a data configuration diagram of the operating state feature amount database 140B according to the third embodiment. The operating state 141B is a combination of the operating states of the electric devices 211 to 213. For example, the third record "cooling, OFF, ..." Indicates that the operating state (setting state) of the electric device 211, which is an air conditioner, is "cooling" and the electric device 212 is "OFF". ing.
 図8に戻って、稼働状態推定部113Bは、稼働状態特徴量データベース140Bを参照して、電気機器210個々の稼働状態を推定する。詳しくは、稼働状態特徴量データベース140Bのレコードのなかで、特徴量抽出部112が抽出した特徴量に最も類似する特徴量をもつレコードを検索する。稼働状態推定部113Bは、検索結果のレコードの稼働状態141Bを、電気機器210個々の稼働状態の推定結果として異常診断部115Bに出力する。
 設定情報収集部114Bは、電気機器210個々の稼働状態を取得する。異常診断部115Bは、推定された電気機器210個々の稼働状態と、設定情報収集部114が取得した電気機器210個々の設定情報とを個々に比較し、差異がある電気機器210があれば当該電気機器が異常であると判定する。
Returning to FIG. 8, the operating state estimation unit 113B estimates the operating state of each electric device 210 with reference to the operating state feature amount database 140B. Specifically, among the records of the operating state feature amount database 140B, the record having the feature amount most similar to the feature amount extracted by the feature amount extraction unit 112 is searched. The operating state estimation unit 113B outputs the operating state 141B of the search result record to the abnormality diagnosis unit 115B as the estimation result of the operating state of each of the electric devices 210.
The setting information collecting unit 114B acquires the operating state of each of the electric devices 210. The abnormality diagnosis unit 115B individually compares the estimated operating state of each electric device 210 with the setting information of each electric device 210 acquired by the setting information collecting unit 114, and if there is a difference, the electric device 210 is concerned. Judge that the electrical equipment is abnormal.
≪第3の実施形態:異常診断処理≫
 図10は、第3の実施形態に係る異常診断処理のフローチャートである。異常診断処理は、所定のタイミング、例えば定期的に実行される。
 ステップS31において電力データ収集部111は、計測装置300から電力データを取得する。
 ステップS32において特徴量抽出部112は、電力データから特徴量を抽出する。
<< Third Embodiment: Abnormality diagnosis processing >>
FIG. 10 is a flowchart of the abnormality diagnosis process according to the third embodiment. The abnormality diagnosis process is executed at a predetermined timing, for example, periodically.
In step S31, the power data collection unit 111 acquires power data from the measuring device 300.
In step S32, the feature amount extraction unit 112 extracts the feature amount from the power data.
 ステップS33において稼働状態推定部113Bは、稼働状態特徴量データベース140Bを参照して、特徴量から電気機器210それぞれの稼働状態を推定する。
 ステップS34において設定情報収集部114Bは、電気機器210それぞれから設定情報を取得する。
 ステップS35において異常診断部115Bは、ステップS33において稼働状態推定部113Bが推定した推定結果と、ステップS34において設定情報収集部114Bが収集した設定情報とを、電気機器210個々に比較する。
In step S33, the operating state estimation unit 113B estimates the operating state of each of the electric devices 210 from the feature amount with reference to the operating state feature amount database 140B.
In step S34, the setting information collecting unit 114B acquires setting information from each of the electric devices 210.
In step S35, the abnormality diagnosis unit 115B compares the estimation result estimated by the operating state estimation unit 113B in step S33 with the setting information collected by the setting information collecting unit 114B in step S34 for each electric device 210.
 ステップS36において異常診断部115Bは、ステップS35における全ての電気機器210について比較結果が一致すれば(ステップS36→YES)異常診断処理を終了し、不一致な電気機器210があれば(ステップS36→NO)ステップS37に進む。
 ステップS37において通知部116は、推定された稼働状態と取得された設定情報とが不一致の電気機器210の異常を通知する。
In step S36, the abnormality diagnosis unit 115B ends the abnormality diagnosis process if the comparison results match for all the electrical devices 210 in step S35 (step S36 → YES), and if there is a mismatched electrical device 210 (step S36 → NO). ) Proceed to step S37.
In step S37, the notification unit 116 notifies the abnormality of the electric device 210 in which the estimated operating state and the acquired setting information do not match.
≪第3の実施形態:異常診断装置の特徴≫
 異常診断装置100Bは、特徴量を参照して複数の電気機器の異常を検出し通知する。特徴量は、電気機器210が正常に稼働しているときに収集されたデータ(図9記載の稼働状態特徴量データベース140B参照)である。異常診断装置100Bは、取得や収集が困難である異常時の特徴量なしに異常診断が可能であり、低コストで実現可能となる。また、異常診断装置100Bは、収集できない特徴量や予想外の異常状態であるときの特徴量がなくても異常状態を検出でき、検出精度が高い。
<< Third Embodiment: Features of the abnormality diagnosis device >>
The abnormality diagnosis device 100B detects and notifies an abnormality of a plurality of electric devices by referring to the feature amount. The feature amount is data collected when the electric device 210 is operating normally (see the operating state feature amount database 140B shown in FIG. 9). The abnormality diagnosis device 100B can perform abnormality diagnosis without a feature amount at the time of abnormality, which is difficult to acquire and collect, and can be realized at low cost. Further, the abnormality diagnosis device 100B can detect the abnormal state even if there is no feature amount that cannot be collected or a feature amount when the abnormal state is unexpected, and the detection accuracy is high.
≪第4の実施形態の概要≫
 第3の実施形態では、特徴量(図9記載の稼働状態特徴量データベース140B参照)は、事前に電気機器210、ないしは同種の電気機器から収集している。これに替えて、異常診断中(電気機器の監視中)に取得するようにしてもよい。
 事前の特徴量の収集が不要となることで、収集する手間やコストを削減できる。また、異常診断装置の導入期間を短縮することができる。
<< Outline of the fourth embodiment >>
In the third embodiment, the feature amount (see the operating state feature amount database 140B shown in FIG. 9) is collected in advance from the electric device 210 or the same type of electric device. Instead of this, it may be acquired during abnormality diagnosis (during monitoring of electrical equipment).
By eliminating the need to collect features in advance, it is possible to reduce the labor and cost of collecting them. In addition, the introduction period of the abnormality diagnosis device can be shortened.
≪第4の実施形態:異常診断装置の構成≫
 図11は、第4の実施形態に係る異常診断装置100Cの機能ブロック図である。第3の実施形態における異常診断装置100B(図8参照)と比較して、制御部110に特徴量設定部117Cが加わる。記憶部130に記憶されるプログラム131Cには、特徴量設定処理(後記する図12参照)の手順が記述される。
<< Fourth Embodiment: Configuration of Abnormality Diagnosis Device >>
FIG. 11 is a functional block diagram of the abnormality diagnosis device 100C according to the fourth embodiment. Compared with the abnormality diagnosis device 100B (see FIG. 8) in the third embodiment, the feature amount setting unit 117C is added to the control unit 110. The program 131C stored in the storage unit 130 describes the procedure of the feature amount setting process (see FIG. 12 described later).
 特徴量設定部117Cは、設定情報収集部114Bから電気機器210の設定情報を取得して、稼働状態特徴量データベース140Bに設定情報に対応する稼働状態141Bであるレコードが格納されているか否かを判定する。格納されていない場合には、特徴量抽出部112が抽出した電力データの特徴量(平均値、時系列値、周波数分布)と、設定情報に対応する稼働状態141Bともつレコードを稼働状態特徴量データベース140Bに追加する。 The feature amount setting unit 117C acquires the setting information of the electric device 210 from the setting information collecting unit 114B, and determines whether or not the record of the operating state 141B corresponding to the setting information is stored in the operating state feature amount database 140B. judge. If it is not stored, a record having the feature amount (average value, time series value, frequency distribution) of the power data extracted by the feature amount extraction unit 112 and the operating state 141B corresponding to the setting information is stored as the operating state feature amount. Add to database 140B.
≪第4の実施形態:特徴量設定処理≫
 図12は、第4の実施形態に係る特徴量設定処理のフローチャートである。特徴量設定処理は、所定のタイミング、例えば、定期的に処理される。特徴量設定処理は、設定情報収集部114Bが設定情報を取得するタイミングで、実行されてもよい。
 ステップS41において特徴量設定部117Cは、設定情報収集部114Bから電気機器210個々の設定情報を取得する。
<< Fourth Embodiment: Feature amount setting process >>
FIG. 12 is a flowchart of the feature amount setting process according to the fourth embodiment. The feature amount setting process is processed at a predetermined timing, for example, periodically. The feature amount setting process may be executed at the timing when the setting information collecting unit 114B acquires the setting information.
In step S41, the feature amount setting unit 117C acquires the setting information of each electric device 210 from the setting information collecting unit 114B.
 ステップS42において特徴量設定部117Cは、稼働状態特徴量データベース140B(図9参照)に、ステップS41において取得した設定情報に対応する稼働状態141Bであるレコードが格納されているか否かを判定する。特徴量設定部117Cは、格納済みなら(ステップS42→YES)ならば特徴量設定処理を終了し、未格納なら(ステップS42→NO)ならステップS43に進む。 In step S42, the feature amount setting unit 117C determines whether or not the record of the operating state 141B corresponding to the setting information acquired in step S41 is stored in the operating state feature amount database 140B (see FIG. 9). The feature amount setting unit 117C ends the feature amount setting process if it is stored (step S42 → YES), and proceeds to step S43 if it is not stored (step S42 → NO).
 ステップS43において電力データ収集部111は、計測装置300から電力データを取得する。
 ステップS44において特徴量抽出部112は、電力データから特徴量を抽出する。
 ステップS45において特徴量設定部117Cは、稼働状態特徴量データベース140Bに新しいレコードを追加する。次に、特徴量設定部117Cは、当該レコードの稼働状態141BをステップS41で取得した電気機器210個々の設定情報に対応する稼働状態とする。また、特徴量設定部117Cは、当該レコードの平均値142、時系列値143、および周波数分布144をステップS44で抽出された平均値、時系列値、および周波数分布とする。
In step S43, the power data collection unit 111 acquires power data from the measuring device 300.
In step S44, the feature amount extraction unit 112 extracts the feature amount from the power data.
In step S45, the feature amount setting unit 117C adds a new record to the operating state feature amount database 140B. Next, the feature amount setting unit 117C sets the operating state 141B of the record to the operating state corresponding to the setting information of each electric device 210 acquired in step S41. Further, the feature amount setting unit 117C sets the average value 142, the time series value 143, and the frequency distribution 144 of the record as the average value, the time series value, and the frequency distribution extracted in step S44.
≪第4の実施形態:異常診断装置の特徴≫
 異常診断装置100Cは、取得した設定情報に対応する電気機器210の稼働状態が、稼働状態特徴量データベース140Bに含まれない場合には、新しい稼働状態(稼働状態の組み合わせ)であると判定して、電力データの特徴量を稼働状態特徴量データベース140Bに加える。このようにすることで、異常診断装置100Cでは、事前の特徴量の収集が不要となり、特徴量を収集する手間やコストを削減できる。また、異常診断装置100Cの導入期間を短縮することができる。
<< Fourth Embodiment: Features of the abnormality diagnosis device >>
If the operating state of the electric device 210 corresponding to the acquired setting information is not included in the operating state feature database 140B, the abnormality diagnosis device 100C determines that it is a new operating state (combination of operating states). , The feature amount of the power data is added to the operating state feature amount database 140B. By doing so, the abnormality diagnosis device 100C does not need to collect the feature amount in advance, and the labor and cost for collecting the feature amount can be reduced. In addition, the introduction period of the abnormality diagnosis device 100C can be shortened.
≪第5の実施形態の概要≫
 第1の実施形態では、異常診断装置100は、電気機器200が異常状態にあることを通知する。第5の実施形態では、異常種別(異常の原因)を含めて通知する。
<< Outline of the fifth embodiment >>
In the first embodiment, the abnormality diagnosis device 100 notifies that the electric device 200 is in an abnormal state. In the fifth embodiment, the notification includes the abnormality type (cause of the abnormality).
≪第5の実施形態:異常診断装置の構成≫
 図13は、第5の実施形態に係る異常診断装置100Dの機能ブロック図である。第1の実施形態における異常診断装置100(図2参照)と比較して、記憶部130には、異常状態特徴量データベース150D(後記する図14参照)が記憶され、制御部110の通知部116Dが異なる。記憶部130に記憶されるプログラム131Dには、異常診断処理(後記する図15参照)の手順が記述される。
<< Fifth Embodiment: Configuration of Abnormality Diagnosis Device >>
FIG. 13 is a functional block diagram of the abnormality diagnosis device 100D according to the fifth embodiment. Compared with the abnormality diagnosis device 100 (see FIG. 2) in the first embodiment, the storage unit 130 stores the abnormality state feature amount database 150D (see FIG. 14 described later), and the notification unit 116D of the control unit 110. Is different. The program 131D stored in the storage unit 130 describes the procedure for the abnormality diagnosis process (see FIG. 15 described later).
 図14は、第5の実施形態に係る異常状態特徴量データベース150Dのデータ構成図である。異常状態特徴量データベース150Dは、稼働状態特徴量データベース140(図3参照)の稼働状態141が異常状態151に替わったデータ構成をしている。異常状態151は、電気機器200の異常状態の種別、ないしは異常の原因を示す。
 通知部116Dは、異常状態特徴量データベース150Dのレコードのなかで、特徴量抽出部112が抽出した特徴量に最も類似する特徴量をもつレコードを検索する。通知部116Dは、検索結果のレコードの異常状態151を、電気機器200における異常状態の原因の推定結果として通知する。通知部116Dは、例えば、時系列値や周波数分布の相関が最も高いレコードの異常状態151を推定結果として出力する。
FIG. 14 is a data structure diagram of the abnormal state feature amount database 150D according to the fifth embodiment. The abnormal state feature amount database 150D has a data structure in which the operating state 141 of the operating state feature amount database 140 (see FIG. 3) is replaced with the abnormal state 151. The abnormal state 151 indicates the type of the abnormal state of the electric device 200 or the cause of the abnormality.
The notification unit 116D searches for a record having a feature amount most similar to the feature amount extracted by the feature amount extraction unit 112 among the records of the abnormal state feature amount database 150D. The notification unit 116D notifies the abnormal state 151 of the search result record as an estimation result of the cause of the abnormal state in the electric device 200. The notification unit 116D outputs, for example, the abnormal state 151 of the record having the highest correlation between the time series value and the frequency distribution as the estimation result.
≪第5の実施形態:異常診断処理≫
 図15は、第5の実施形態に係る異常診断処理のフローチャートである。異常診断処理は、所定のタイミング、例えば定期的に実行される。
 ステップS51~S56は、ステップS11~S16(図4参照)とそれぞれ同様である。
 ステップS57において通知部116Dは、電気機器200の異常の原因を推定する。
 ステップS58において通知部116Dは、ステップS57で推定された異常の原因を含めて電気機器200の異常を通知する。
<< Fifth Embodiment: Abnormality diagnosis processing >>
FIG. 15 is a flowchart of the abnormality diagnosis process according to the fifth embodiment. The abnormality diagnosis process is executed at a predetermined timing, for example, periodically.
Steps S51 to S56 are the same as steps S11 to S16 (see FIG. 4), respectively.
In step S57, the notification unit 116D estimates the cause of the abnormality of the electric device 200.
In step S58, the notification unit 116D notifies the abnormality of the electric device 200 including the cause of the abnormality estimated in step S57.
≪第5の実施形態:異常診断装置の特徴≫
 異常診断装置100Dは、異常と判定された電気機器200について、異常の原因を含めて通知する。電気機器200の管理者は、異常の原因を知ることで、効率的に電気機器200の異常に対応することができるようになる。
 なお、第5の実施形態では、通知部116Dが異常の原因を推定しているが、異常診断部115が推定してもよい。また、異常状態の特徴量として、電力データの平均値、時系列値、周波数分布を用いているが、このなかでも周波数分布、特に高周波の分布(相関)を見て推定するのが望ましい。
<< Fifth Embodiment: Features of the abnormality diagnosis device >>
The abnormality diagnosis device 100D notifies the electric device 200 determined to be abnormal including the cause of the abnormality. By knowing the cause of the abnormality, the manager of the electric device 200 can efficiently deal with the abnormality of the electric device 200.
In the fifth embodiment, the notification unit 116D estimates the cause of the abnormality, but the abnormality diagnosis unit 115 may estimate the cause. In addition, the average value, time series value, and frequency distribution of power data are used as the feature quantities of the abnormal state. Among them, it is desirable to estimate by looking at the frequency distribution, especially the high frequency distribution (correlation).
≪変形例:機械学習技術を用いた稼働状態の推定≫
 上記した実施形態では、稼働状態推定部113,113Bは、稼働状態特徴量データベース140,140B(図3および図9参照)のなかで、電力データの特徴量に最も近い特徴量のレコードを検索する。稼働状態推定部113,113Bは、当該レコードの稼働状態141,141Bを推定結果とする。このような処理に替えて、機械学習技術を用いてもよい。
<< Modification example: Estimating the operating state using machine learning technology >>
In the above-described embodiment, the operating state estimation units 113 and 113B search for the record of the feature amount closest to the feature amount of the power data in the operating state feature amount databases 140 and 140B (see FIGS. 3 and 9). .. The operating state estimation units 113 and 113B use the operating states 141 and 141B of the record as the estimation result. Instead of such processing, machine learning techniques may be used.
 図16は、第1の実施形態の変形例に係る異常診断装置100Eの機能ブロック図である。第1の実施形態の異常診断装置100(図2参照)と比較して、制御部110に学習部117Eが追加され、稼働状態推定部113Eが異なる。また、記憶部130に稼働状態特徴量データベース140に替わり、稼働状態学習モデル140Eが記憶される。 FIG. 16 is a functional block diagram of the abnormality diagnosis device 100E according to the modified example of the first embodiment. Compared with the abnormality diagnosis device 100 (see FIG. 2) of the first embodiment, the learning unit 117E is added to the control unit 110, and the operating state estimation unit 113E is different. Further, the operating state learning model 140E is stored in the storage unit 130 instead of the operating state feature amount database 140.
 稼働状態学習モデル140Eは、電力データの特徴量に電気機器200,210の稼働状態141,141Bを正解ラベルとして付与した教師データを用いて訓練された機械学習モデルである。機械学習技術としては、ニューラルネットワークやSVM(Support Vector Machine)、クラスタリングなどを用いることができる。稼働状態の推定の他に、通知部116D(図13参照)が行う異常原因の推定に、機械学習技術を用いてもよい。 The operating state learning model 140E is a machine learning model trained using teacher data in which the operating states 141 and 141B of the electric devices 200 and 210 are added as correct labels to the feature quantities of the electric power data. As the machine learning technique, a neural network, SVM (Support Vector Machine), clustering, or the like can be used. In addition to the estimation of the operating state, the machine learning technique may be used for the estimation of the cause of the abnormality performed by the notification unit 116D (see FIG. 13).
 稼働状態推定部113Eは、稼働状態学習モデル140Eを参照して、電力データの特徴量から電気機器200,210の稼働状態を推定する。設定情報収集部114が、稼働状態学習モデル140Eの推定結果にない設定情報(稼働状態)を取得すると、学習部117Eは、取得時点での電力データの特徴量に対して当該設定情報を正解とする教師データを追加で学習(訓練)して、稼働状態学習モデル140Eを更新する。 The operating state estimation unit 113E estimates the operating states of the electric devices 200 and 210 from the feature amount of the electric power data with reference to the operating state learning model 140E. When the setting information collecting unit 114 acquires the setting information (operating state) that is not in the estimation result of the operating state learning model 140E, the learning unit 117E determines that the setting information is correct for the feature amount of the power data at the time of acquisition. The operating state learning model 140E is updated by additionally learning (training) the teacher data to be used.
≪変形例:ウェーブレットを用いた稼働状態の推定≫
 複数の電気機器210(図7参照)の稼働状態を推定する場合、稼働状態特徴量データベース140B(図9参照)のレコード数は、各電気機器210における稼働状態の数の積になるため、電気機器210の増加に伴い指数関数的に大きくなる。これに対して、各電気機器210の稼働状態ごとに、電力データのウェーブレット変換後に最大値をもつ展開係数のシフト数と値とを特徴量として登録(記憶)するようにしてもよい。推定時には、電力データのウェーブレット変換後の展開係数を求め、特徴データにあるシフト数と値との対応をとることでそれぞれの電気機器210の稼働状態が推定できる。
<< Modification example: Estimating the operating state using wavelets >>
When estimating the operating state of a plurality of electric devices 210 (see FIG. 7), the number of records in the operating state feature amount database 140B (see FIG. 9) is the product of the number of operating states in each electric device 210. It increases exponentially as the number of devices 210 increases. On the other hand, for each operating state of each electric device 210, the shift number and the value of the expansion coefficient having the maximum value after the wavelet transform of the electric power data may be registered (stored) as a feature amount. At the time of estimation, the operating state of each electric device 210 can be estimated by obtaining the expansion coefficient of the power data after wavelet transform and taking the correspondence between the number of shifts and the value in the feature data.
 このような特徴量を用いることで、特徴量の数(稼働状態特徴量データベース140Bのレコード数)は、各電気機器210における稼働状態の数の和になり、電気機器210の数に比例する。登録される特徴量の数が削減でき、延いては推定処理の速度が向上する。また、特徴量を収集するコストを削減することができる。なお、同様にして通知部116D(図13参照)は、電気機器の異常状態(異常の原因)を推定してもよい。 By using such a feature amount, the number of feature amounts (the number of records in the operating state feature amount database 140B) is the sum of the number of operating states in each electric device 210, and is proportional to the number of electric devices 210. The number of registered features can be reduced, which in turn improves the speed of estimation processing. In addition, the cost of collecting features can be reduced. Similarly, the notification unit 116D (see FIG. 13) may estimate the abnormal state (cause of the abnormality) of the electric device.
≪変形例:特徴量≫
 上記した実施形態において、稼働状態特徴量データベース140は、電力データの平均値142、時系列値143、周波数分布144の全てではなく、一部のみを含む形態であってもよい。この場合、稼働状態推定部113,113Bは、この一部の特徴量を比較して稼働状態を推定する。
≪Variation example: Feature amount≫
In the above-described embodiment, the operating state feature amount database 140 may include only a part of the average value 142, the time series value 143, and the frequency distribution 144 of the power data. In this case, the operating state estimation units 113 and 113B estimate the operating state by comparing some of the features.
≪プログラム≫
 上記した実施形態では、プログラム131,131A,131B,131C,131D,131Eは、コンピュータである異常診断装置100,100A,100B,100C,100D,100Eに記憶される。記録媒体910にあるプログラム131,131A,131B,131C,131D,131Eが読み込まれて、記憶部130にロードされて実行されてもよいし、記録媒体910からインストールされて実行されてもよい。
 図17は、上記した実施形態に係る記録媒体910を示す図である。記録媒体910からコンピュータ900に、インストールを行うことで、コンピュータが異常診断装置100,100A,100B,100C,100D,100Eとして機能することができるようになる。
≪Program≫
In the above-described embodiment, the programs 131, 131A, 131B, 131C, 131D, 131E are stored in the abnormality diagnosis devices 100, 100A, 100B, 100C, 100D, 100E, which are computers. The programs 131, 131A, 131B, 131C, 131D, 131E in the recording medium 910 may be read and loaded into the storage unit 130 and executed, or may be installed and executed from the recording medium 910.
FIG. 17 is a diagram showing a recording medium 910 according to the above-described embodiment. By installing the recording medium 910 on the computer 900, the computer can function as the abnormality diagnostic devices 100, 100A, 100B, 100C, 100D, 100E.
≪その他の変形例≫
 以上、本発明のいくつかの実施形態について説明したが、これらの実施形態は、例示に過ぎず、本発明の技術的範囲を限定するものではない。例えば、上記した実施形態では、電力データの特徴量として、平均値や時系列値、周波数分布(ウェーブレット変換後の展開係数を含む)などをあげたが、瞬間最大値など他の特徴量であってもよい。
 また、上記した実施形態では、特徴量(稼働状態特徴量データベース)は、異常診断装置の記憶部に記憶されるが、これに限らない。稼働状態推定部は、外部に記憶される特徴量を参照して、電気機器の稼働状態を推定するようにしてもよい。
≪Other variants≫
Although some embodiments of the present invention have been described above, these embodiments are merely examples and do not limit the technical scope of the present invention. For example, in the above-described embodiment, the average value, the time series value, the frequency distribution (including the expansion coefficient after the wavelet transform) and the like are given as the feature amount of the power data, but other feature amounts such as the instantaneous maximum value are used. You may.
Further, in the above-described embodiment, the feature amount (operating state feature amount database) is stored in the storage unit of the abnormality diagnosis device, but the present invention is not limited to this. The operating state estimation unit may estimate the operating state of the electric device by referring to the feature amount stored in the outside.
 本発明はその他の様々な実施形態をとることが可能であり、さらに、本発明の要旨を逸脱しない範囲で、省略や置換等種々の変更を行うことができる。これら実施形態やその変形は、本明細書等に記載された発明の範囲や要旨に含まれるとともに、特許請求の範囲に記載された発明とその均等の範囲に含まれる。 The present invention can take various other embodiments, and further, various changes such as omission and replacement can be made without departing from the gist of the present invention. These embodiments and variations thereof are included in the scope and gist of the invention described in the present specification and the like, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.
10,10B 異常診断システム
100,100A,100B,100C,100D,100E 異常診断装置
111 電力データ収集部
112 特徴量抽出部
113,113B,113E 稼働状態推定部
114,114B 設定情報収集部
115,115B 異常診断部
116,116D 通知部
117,117C 特徴量設定部
117E 学習部
140,140B 稼働状態特徴量データベース(稼働状態特徴量データ)
140E 稼働状態学習モデル(機械学習モデル)
150D 異常状態特徴量データベース(故障特徴量データ)
200,210,211,212,213 電気機器
10,10B Abnormality diagnosis system 100, 100A, 100B, 100C, 100D, 100E Abnormality diagnosis device 111 Power data collection unit 112 Feature quantity extraction unit 113, 113B, 113E Operation status estimation unit 114, 114B Setting information collection unit 115, 115B Abnormality Diagnosis unit 116, 116D Notification unit 117, 117C Feature amount setting unit 117E Learning unit 140, 140B Operating state feature amount database (operating state feature amount data)
140E Operating state learning model (machine learning model)
150D Abnormal state feature database (fault feature data)
200, 210, 211,212,213 Electrical equipment

Claims (11)

  1.  電気機器の電力データを収集する電力データ収集部と、
     収集された前記電力データから特徴量を取得する特徴量抽出部と、
     前記電気機器の稼働状態と当該稼働状態における電力データの特徴量との関係を含む稼働状態特徴量データ、および取得された特徴量から前記電気機器の稼働状態を推定する稼働状態推定部と、
     前記電気機器の稼働状態を設定する設定情報を収集する設定情報収集部と、
     推定された稼働状態と、収集された設定情報とが対応しないならば、前記電気機器が異常と判定する異常診断部と、を備える
     ことを特徴とする異常診断装置。
    The power data collection unit that collects power data of electrical equipment,
    A feature amount extraction unit that acquires a feature amount from the collected power data,
    The operating state feature amount data including the relationship between the operating state of the electric device and the feature amount of the electric power data in the operating state, the operating state estimation unit that estimates the operating state of the electric device from the acquired feature amount, and the operating state estimation unit.
    A setting information collection unit that collects setting information that sets the operating status of the electrical equipment,
    An abnormality diagnosis device including an abnormality diagnosis unit that determines that the electric device is abnormal if the estimated operating state and the collected setting information do not correspond to each other.
  2.  前記稼働状態特徴量データは、前記電気機器の稼働状態と当該稼働状態における電力データの特徴量とが関連付けられて格納される稼働状態特徴量データベースであり、
     前記稼働状態推定部は、前記稼働状態特徴量データベースを参照して、取得された特徴量に対応する前記電気機器の稼働状態を推定する
     ことを特徴とする請求項1に記載の異常診断装置。
    The operating state feature amount data is an operating state feature amount database in which the operating state of the electric device and the feature amount of the electric power data in the operating state are stored in association with each other.
    The abnormality diagnosis device according to claim 1, wherein the operating state estimation unit estimates the operating state of the electric device corresponding to the acquired feature amount by referring to the operating state feature amount database.
  3.  収集された設定情報に対応する稼働状態が、前記稼働状態特徴量データベースに格納されていない場合に、当該設定情報に対応する稼働状態と、当該設定情報が収集された時点で収集された電力データの特徴量とを関連付けて、前記稼働状態特徴量データベースに格納する特徴量設定部をさらに備える
     ことを特徴とする請求項2に記載の異常診断装置。
    When the operating status corresponding to the collected setting information is not stored in the operating status feature database, the operating status corresponding to the setting information and the power data collected at the time when the setting information is collected. The abnormality diagnosis device according to claim 2, further comprising a feature amount setting unit that is stored in the operating state feature amount database in association with the feature amount of the above.
  4.  前記稼働状態特徴量データが含む稼働状態は、1つまたは複数の前記電気機器の稼働状態であり、
     前記稼働状態推定部は、複数の前記電気機器それぞれの稼働状態を推定し、
     前記設定情報収集部は、複数の前記電気機器の設定情報を収集し、
     前記異常診断部は、複数の前記電気機器それぞれについて、推定された稼働状態と、収集された設定情報との対応をとり、対応していない電気機器が異常と判定する
     ことを特徴とする請求項1に記載の異常診断装置。
    The operating state included in the operating state feature amount data is the operating state of one or more of the electric devices.
    The operating state estimation unit estimates the operating state of each of the plurality of electric devices, and determines the operating state of each of the plurality of electric devices.
    The setting information collecting unit collects setting information of a plurality of the electric devices, and collects setting information.
    The claim is characterized in that the abnormality diagnosis unit takes correspondence between the estimated operating state and the collected setting information for each of the plurality of the electric devices, and determines that the non-corresponding electric device is abnormal. The abnormality diagnostic device according to 1.
  5.  前記稼働状態特徴量データは、前記電気機器の電力データの特徴量に対して当該電気機器の稼働状態を正解ラベルとして付与された教師データを用いて訓練された機械学習モデルであり、
     前記稼働状態推定部は、前記機械学習モデルを用いて、取得された特徴量から前記電気機器の稼働状態を推定する
     ことを特徴とする請求項1に記載の異常診断装置。
    The operating state feature amount data is a machine learning model trained using teacher data assigned to the feature amount of the power data of the electric device with the operating state of the electric device as a correct answer label.
    The abnormality diagnosis device according to claim 1, wherein the operating state estimation unit estimates the operating state of the electric device from the acquired feature amount by using the machine learning model.
  6.  収集された前記設定情報に対応する稼働状態が、前記機械学習モデルの推定結果に含まれない場合に、当該設定情報が収集された時点で収集された電力データの特徴量に対して当該設定情報に対応する稼働状態を正解ラベルとして付与された教師データを用いて、前記機械学習モデルを追加訓練する学習部をさらに備える
     ことを特徴とする請求項5に記載の異常診断装置。
    When the operating state corresponding to the collected setting information is not included in the estimation result of the machine learning model, the setting information is relative to the feature amount of the power data collected at the time when the setting information is collected. The abnormality diagnosis device according to claim 5, further comprising a learning unit for additional training of the machine learning model using the teacher data assigned with the operating state corresponding to the correct answer label.
  7.  前記機械学習モデルは、複数の前記電気機器の電力データの特徴量に対して複数の当該電気機器の稼働状態を正解ラベルとして付与された教師データを用いて訓練された機械学習モデルであって、
     前記稼働状態推定部は、前記機械学習モデルを用いて、取得された特徴量から複数の前記電気機器の稼働状態を推定し、
     前記異常診断部は、推定された稼働状態と、収集された設定情報とが対応しない電気機器があるときに対応していない電気機器を異常と判定する
     ことを特徴とする請求項5に記載の異常診断装置。
    The machine learning model is a machine learning model trained using teacher data assigned with the operating states of a plurality of the electric devices as correct answer labels for the feature quantities of the electric power data of the plurality of the electric devices.
    The operating state estimation unit estimates the operating states of a plurality of the electric devices from the acquired features by using the machine learning model.
    The fifth aspect of claim 5, wherein the abnormality diagnosis unit determines an electric device that does not correspond to the estimated operating state and the collected setting information when there is an electric device that does not correspond to the estimated operating state. Abnormality diagnostic device.
  8.  前記異常診断部は、前記電気機器の異常原因と当該異常原因における電力データの特徴量との関係を含む故障特徴量データ、および取得された特徴量から前記電気機器の異常原因を推定する
     ことを特徴とする請求項1に記載の異常診断装置。
    The abnormality diagnosis unit estimates the cause of the abnormality of the electric device from the failure feature amount data including the relationship between the cause of the abnormality of the electric device and the feature amount of the power data in the cause of the abnormality, and the acquired feature amount. The abnormality diagnostic apparatus according to claim 1.
  9.  前記電力データの特徴量は、消費電力量、消費電力、電圧、電流、および力率の平均値、瞬間最大値、時系列データ、および時系列データの周波数分布の何れか少なくとも1つを含む
     ことを特徴とする請求項1に記載の異常診断装置。
    The feature amount of the power data includes at least one of power consumption, power consumption, voltage, current, and average value of power factor, instantaneous maximum value, time series data, and frequency distribution of time series data. The abnormality diagnosis device according to claim 1, wherein the abnormality diagnosis device is characterized.
  10.  コンピュータを請求項1~9の何れか1項に記載の異常診断装置として機能させるためのプログラム。 A program for making a computer function as an abnormality diagnostic device according to any one of claims 1 to 9.
  11.  異常診断装置の異常診断方法であって、
     前記異常診断装置は、
     電気機器の電力データを収集するステップと、
     収集された前記電力データから特徴量を取得するステップと、
     前記電気機器の稼働状態と当該稼働状態における電力データの特徴量との関係を含む稼働状態特徴量データ、および取得された特徴量から前記電気機器の稼働状態を推定するステップと、
     前記電気機器の稼働状態を設定する設定情報を収集するステップと、
     推定された稼働状態と、収集された設定情報とが対応しないならば、前記電気機器が異常と判定するステップと、を実行する
     ことを特徴とする異常診断方法。
    It is an abnormality diagnosis method of the abnormality diagnosis device.
    The abnormality diagnosis device is
    Steps to collect power data for electrical equipment and
    Steps to acquire features from the collected power data,
    The operating state feature amount data including the relationship between the operating state of the electric device and the feature amount of the electric power data in the operating state, and the step of estimating the operating state of the electric device from the acquired feature amount.
    A step of collecting setting information for setting the operating state of the electric device, and
    An abnormality diagnosis method comprising executing a step of determining an abnormality by the electric device if the estimated operating state and the collected setting information do not correspond to each other.
PCT/JP2020/047483 2020-12-18 2020-12-18 Abnormality diagnosis device, program and abnormality diagnosis method WO2022130626A1 (en)

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