WO2017150263A1 - Dispositif de détection d'anomalie, système de détection d'anomalie, et procédé correspondant - Google Patents

Dispositif de détection d'anomalie, système de détection d'anomalie, et procédé correspondant Download PDF

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WO2017150263A1
WO2017150263A1 PCT/JP2017/006258 JP2017006258W WO2017150263A1 WO 2017150263 A1 WO2017150263 A1 WO 2017150263A1 JP 2017006258 W JP2017006258 W JP 2017006258W WO 2017150263 A1 WO2017150263 A1 WO 2017150263A1
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abnormality
determination
prediction
unit
prediction determination
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Japanese (ja)
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進吾 足立
信補 高橋
基朗 小熊
剛 武本
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Hitachi Ltd
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Hitachi Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use

Definitions

  • the present invention relates to an anomaly detection device, an anomaly detection system, and a method thereof.
  • Non-Patent Document 1 calculates the predicted value of the next time from the observed value of the past time by applying support vector regression to the measured value time series of the flow rate and pressure of the water distribution process.
  • Disclosed is a method for detecting an abnormality in a water distribution process, such as the occurrence of water leakage, by performing property detection (Novelty detection).
  • Non-Patent Document 1 When performing abnormality detection of the water distribution process such as the occurrence of water leakage by the method of Non-Patent Document 1, it is impossible to detect abnormality such as the occurrence of small-scale water leakage.
  • This method uses a measurement value affected by the occurrence of water leakage to calculate a predicted value at the next time after the occurrence of water leakage. For this reason, in the case of small-scale water leakage, the predicted value is close to the measured value after the occurrence of water leakage, and the difference between the predicted value and the measured value is reduced. Therefore, if the water leak is large enough that the difference between the predicted value and the measured value is sufficiently large immediately after the occurrence of water leakage, the predicted value can be detected before being affected by the occurrence of water leakage, but small-scale water leakage cannot be detected.
  • the scale of the water leakage is a scale obtained by evaluating the change that the water leakage gives to the measured values of the sensor such as the flow rate and the pressure by a relative ratio to the fluctuation of the measured values in the normal time when no water leakage occurs. Even if the water flow leaks from the water pipe with a large absolute value of the flow rate, if the water leak occurs in a distribution area with a large amount of water distribution, the water leak will be small.
  • the disclosed abnormality detection device uses each of the plurality of prediction determination methods, based on the measurement value of the sensor of the monitored process in the data range according to the plurality of prediction determination methods, the first sensor of the determination time Predict the predicted value of the measured value, (1) the difference between the predicted value and the first measured value at the determination time, and (2) the first measured value outside the range between the upper limit value and the lower limit value of the predicted value
  • a prediction determination unit that determines an abnormality of the monitoring target process and outputs an abnormality determination result of determining an abnormality of a plurality of prediction determination methods, a second that an abnormality included in the abnormality determination result affects
  • the influence data selection unit that selects the range including the measured value of the measurement data as the influence data range as the influence data range, among the abnormality determination results, the reliability of the abnormality determination result based on the influence data included in the influence data range is lowered, and a plurality of Predictive judgment Having the abnormality determination integrating unit that integrates the determination result, and an output
  • the disclosed abnormality detection device even a process abnormality that gives a gradual change in the measured value can be detected.
  • FIG. 1 is a configuration diagram of an abnormality detection system 100 that monitors a water distribution process (water pipe network) based on sensor measurement values such as flow rate and pressure to detect an abnormality such as occurrence of water leakage.
  • the abnormality detection system 100 includes an abnormality detection device 101, a sensor 191 that obtains measurement values such as flow rate and pressure, a measurement value collection device 102, and an alarm display device 103.
  • the abnormality detection apparatus 101 includes an influence data selection unit 111, a determination integration unit 112, a prediction determination unit 131, a prediction determination unit 132, a measurement value collection unit 151, and an output unit 152, and a measurement value storage unit 121.
  • Each storage unit includes an auxiliary data storage unit 122 and a prediction determination method data storage unit 123.
  • the influence data selection unit 111 inputs an abnormality determination result from the prediction determination units 131 and 132 (hereinafter, the prediction determination unit 131 is representative when the prediction determination units 131 and 132 need not be individually described), and the abnormality If it is determined that the determination result is abnormal, a data range of measurement values affected by the abnormality is selected, and the selected data range is output to the determination integration unit 112 as an influence data range (described later). Details of the processing of the influence data selection unit 111 will be described later.
  • the determination integration unit 112 inputs the abnormality determination result from the prediction determination unit 131, inputs the influence data range from the influence data selection unit 111, and lowers the reliability (described later) of the abnormality determination result based on the input influence data range.
  • the abnormality determination result is integrated, and the integrated abnormality determination result is output to the output unit 152. Details of the processing of the determination integration unit 112 will be described later.
  • the prediction determination unit 131 reads measurement values from the measurement value storage unit 121, reads auxiliary data from the auxiliary data storage unit 122 as necessary, and reads prediction determination method data from the prediction determination method data storage unit 123.
  • the prediction determination unit 131 uses a predetermined prediction determination method to calculate a sensor predicted value (a value that the sensor will output as a measurement value at the prediction time) from the sensor measurement value in the data range defined by the prediction determination method data. The difference between the predicted value and the measured value of the predicted time (details will be described later, but the predicted value has the upper and lower limit values of the predicted value itself and the predicted value.
  • the prediction determination unit 132 also outputs an abnormality determination result based on a prediction determination method different from the prediction determination unit 131.
  • the prediction determination unit 131 adjusts the prediction determination method parameters (prediction determination method data) based on each input data including the measurement values, and sets the adjusted prediction determination method parameters. Stored in the prediction determination method data storage unit 123.
  • the prediction determination unit 131 learns the prediction determination method, and executes prediction processing of the predicted value of the sensor, and processing for determining abnormality of the monitored water pipe network from the difference between the predicted value and the measured value.
  • the prediction determination unit 131 uses a known technique such as a method using support vector regression or a neural network described in Non-Patent Document 1 for prediction and abnormality determination. Details of the processing of the prediction determination unit 131 will be described later.
  • the abnormality detection apparatus 101 may include a plurality of prediction determination units 131 and 132 in order to perform abnormality determination using different prediction determination methods.
  • the prediction determination part 131 may implement
  • the measurement value storage unit 121 stores the measurement value of the sensor installed in the monitored water pipe network from the measurement value collection unit 151.
  • the prediction determination unit 131 reads the measurement value from the measurement value storage unit 121.
  • the auxiliary data storage unit 122 stores auxiliary data used for the processing of the prediction determination unit 131.
  • the prediction determination unit 131 reads auxiliary data from the auxiliary data storage unit 122.
  • the auxiliary data storage unit 122 is a water pipe network to be monitored, such as a calendar, weather, social event, etc. for specifying the season, month, day of the week, etc., as auxiliary data for prediction determination by the prediction determination unit 131 Information that influences the state of is stored in advance.
  • the abnormality detection device 101 may collect these pieces of information from other devices outside the abnormality detection system 100 and store them in the auxiliary data storage unit 122.
  • the prediction determination method data storage unit 123 stores information such as parameters for determining each prediction determination method for a plurality of prediction determination methods used by the prediction determination unit 131.
  • the prediction determination unit 131 reads information on each prediction determination method from the prediction determination method data storage unit 123.
  • the prediction determination method data storage unit 123 receives and stores parameters of each prediction determination method adjusted by the prediction determination unit 131 through learning.
  • the information such as the parameters stored in the prediction determination method data storage unit 123 includes, for each prediction determination method, data relating to the prediction determination method ID, the adjusted parameter of the prediction determination method, and the measurement value used for the adjustment of this parameter. Range (described later).
  • the measurement value collection unit 151 receives the measurement values of the sensor 191 such as the pressure and flow rate installed in the monitored water pipe network from the measurement value collection device 102 and stores them in the measurement value storage unit 121.
  • the output unit 152 inputs the abnormality determination result integrated from the determination integration unit 112, and presents the abnormality determination result to the operator of the abnormality detection system 100. Further, the output unit 152 outputs an abnormality determination result to the alarm display device 103.
  • the output unit 152 displays the abnormality determination result on the display for the operator.
  • a smart device such as a smartphone or tablet held by the operator is used as the alarm display device 103, and an abnormality determination result is transmitted in response to a request from such a smart device.
  • the output unit 152 may notify the alarm display device 103 by a push type such as an email or an alarm. Good.
  • the measurement value collection device 102 collects measurement values from the sensor 191 that measures the state of the monitored water pipe network, and transmits the collected measurement values of the sensor 191 to the abnormality detection device 101.
  • the alarm display device 103 receives the abnormality determination result from the output unit 152 and displays the received abnormality determination result. Further, the alarm display device 103 may receive a notification of the abnormality determination result from the output unit 152 and display the abnormality determination result.
  • the abnormality detection apparatus 101 may be arrange
  • FIG. 2 is a hardware configuration example of the abnormality detection apparatus 101.
  • the abnormality detection apparatus 101 connects a CPU 201, a memory 202, a media input / output unit 203, an input unit 205, a communication control unit 204 connected to a network, a display unit 206 such as a display, and a peripheral device IF unit 207 via a bus 210. It is a so-called computer.
  • the CPU 201 executes the processing of each processing unit, and the memory 202 stores the program of each processing unit and the data of each storage unit.
  • abnormality detection apparatus 101 the measurement value collection apparatus 102, and the alarm display apparatus 103 may be implemented as different programs on the same computer.
  • FIG. 3 is a configuration example of a water pipe network to be monitored by the abnormality detection apparatus 101.
  • the water pipe network is composed of a plurality of DMAs (Distributed Metered Areas).
  • the DMA is an area of the water pipe network, and there are a small number of pipes (inflow / outflow pipes) into and out of the adjacent pipe network (inflow / outlet pipes), and in many cases there is only one. It is measured.
  • FIG. 3 is a configuration example of a water pipe network supplied from the distribution reservoir 301 and includes DMAs 340 and 341.
  • This water pipe network includes pipes such as the distribution reservoir 301 and the distribution pipe 351, and is composed of sensors such as a flow sensor 310 (rectangle in the figure) and a pressure sensor 320 (o in the figure), and incidental equipment such as a valve 361. Has been.
  • the DMA 340 has one inflow / outflow pipe in which a flow rate sensor 311 is installed, and a pipe valve 361 connected to an adjacent area (DMA 341) is closed. Further, pressure sensors 320 to 322 are installed in the DMA 340.
  • the DMA 341 has an inflow / outflow pipe in which a flow rate sensor 312 and a flow rate sensor 313 are installed.
  • the sensors 191 for collecting the measurement values by the measurement value collection device 102 are flow rate sensors 310-313 and pressure sensors 320-324.
  • FIG. 4 is a diagram showing prediction and abnormality determination by two prediction determination methods different from the measurement value of the flow rate sensor 311. Processing of the prediction determination unit 131 and the influence data selection unit 111 will be described with reference to FIG.
  • the prediction determination unit 131 is activated at a collection period of measurement values of the sensor 311, for example, at a period of 5 minutes.
  • the prediction determination unit 131 reads the measurement value of the sensor 311 from the measurement value storage unit 121, reads auxiliary data from the auxiliary data storage unit 122, and receives each prediction determination method data from the prediction determination method data storage unit 123. read out.
  • the prediction determination unit 131 obtains a prediction value corresponding to the latest measurement value of the sensor 311 using each prediction determination method as a prediction process.
  • the prediction determination unit 131 determines whether or not an abnormality has occurred in the monitored water pipe network based on the difference between the predicted value using each prediction determination method and the latest measured value.
  • the prediction determination unit 131 outputs an abnormality determination result using each prediction determination method to the influence data selection unit 111 and the determination integration unit 112 as a result output process.
  • the prediction determination results 491 and 492 are based on the prediction determination method 1 and the prediction determination method 2 output by the prediction determination unit 131.
  • the horizontal axis represents time
  • the vertical axis represents the measurement value (flow rate) of the flow sensor 311.
  • Circles 401 to 404 in the figure are measurement values of the flow sensor 311 at each time for each measurement value collection cycle. Note that the measurement values that the prediction determination unit 131 determines to be normal in each prediction determination method are white circles, and the measurement values that are determined to be abnormal are black circles.
  • the time series prediction values 411 and 412 are point prediction results by each prediction determination method, and the time series prediction upper limit values 421 and 422 and the prediction lower limit values 431 and 432 are section prediction results by each prediction determination method.
  • the point prediction result is the time-series data of the prediction value at each prediction time (predicted time), and the interval prediction result has a prediction upper limit value determined by the prediction accuracy because the prediction accuracy of the prediction value differs depending on each prediction determination method. It is time series data of a prediction lower limit. The difference between the prediction upper limit value and the prediction lower limit value is called an interval.
  • Prediction determination method 1 performs prediction and determination based on measured values in a time range (time zone) up to the latest time, as in the method described in Non-Patent Document 1.
  • the prediction determination unit 131 uses the measurement value of the time range 470 for prediction of the determination time (same as the above-described prediction time) 480, and the determination time 481
  • the measurement value in the time range 471 is used for the prediction.
  • the predicted value corresponding to the measured value at the determination time is predicted using the measured value in the time range up to 5 minutes before the determination time (the latest measurement value collection time).
  • Prediction determination method 2 performs prediction and determination based on measurement values in a time range before a predetermined time from the determination time. In other words, there is a predetermined time between the time when the last measured value in the time range is obtained and the determination time.
  • the prediction determination unit 131 calculates the measurement value of the time range 475 for prediction of the determination time 480 and measures the time range 476 for prediction of the determination time 481. Use each value. For example, the measurement value at the determination time is predicted using the measurement value 75 minutes before the determination time.
  • the predicted value at the determination time is a value predicted to be obtained as a measurement value at the determination time
  • the upper limit of the prediction accuracy (vibration width) of the predicted value is It is a prediction upper limit
  • a lower limit is a prediction lower limit.
  • a plurality of prediction determination methods are used by setting different times as the predetermined time of the prediction determination method based on the measurement values in the time range before the determination time.
  • the prediction determination method 1 is a method in which the predetermined time is 5 minutes
  • the prediction determination method 2 is a method in which the predetermined time is, for example, 75 minutes. Further, by changing the time range (changing the number of measurement values used for prediction), prediction determination methods with different prediction accuracy can be obtained.
  • the predetermined time used by the prediction determination method is used as a parameter, and the time range or the number of measured values is stored as a data range in the prediction determination method data storage unit 123 in association with the ID of the prediction determination method.
  • the prediction determination unit 131 uses the prediction determination method 1 and the prediction determination method 2 when the difference between the measured value and the predicted value at the determination time is equal to or greater than a predetermined threshold, and when the measured value deviates from the section prediction (upper limit value). If it exceeds or falls below the lower limit), it is determined as abnormal.
  • FIG. 4 shows data (measured values and predicted values) in which water leakage occurred in the DMA 340 immediately before the measurement time of the measured value 401.
  • the prediction determination unit 131 using the prediction determination method 1 determines that the measurement values 401 to 404 are normal because the measured values 401 to 404 are between the prediction upper limit value 421 and the prediction lower limit value 431 (section).
  • the prediction determination unit 131 using the prediction determination method 2 determines that the measurement value 402-404 exceeds the prediction upper limit value 422, and thus is abnormal.
  • the prediction determination method for the measurement value of the flow sensor 311 is described, but a value obtained by applying various processes such as filtering such as moving average and normalization may be used. Moreover, it is good also as performing prediction and determination which considered the correlation with the measured value of another sensor.
  • the prediction determination unit 131 determines that an abnormality has occurred, the prediction determination unit 131 also estimates the abnormality attribute including the abnormality occurrence time, type, location, occurrence location, and the like. Even when it is difficult to estimate each attribute included in the abnormal attribute, the prediction determination unit 131 estimates at least one attribute such as an occurrence time. In the prediction determination method 2, the measured value of the flow sensor 311 that measures the amount of flow into the DMA 340 continuously exceeds the predicted value from the time 480, indicating the characteristic of water leakage. For this reason, the prediction determination unit 131 determines that the abnormality occurrence time is around the determination time 480, the abnormality type is water leakage, the abnormality location is DMA 340, and the abnormality occurrence location is near the flow sensor 311. The prediction determination unit 131 uses a technique such as pattern matching of the increasing tendency of the difference between the predicted value and the measured value as described above in order to estimate the abnormal attribute.
  • the influence data selection unit 111 inputs the abnormality determination result and the abnormality attribute from the prediction determination unit 131. If the abnormality determination result is abnormal, the influence data selection unit selects the influence data range affected by the abnormality, and selects the selected influence data range. The result is output to the determination integration unit 112.
  • the selection process of the influence data selection unit 111 selects the influence data range affected by this abnormality based on the abnormality attribute input from the prediction determination unit 131.
  • the influence data selection unit 111 uses the position of the abnormality type and the location where the abnormality occurs and the measured value of the sensor as the influence data affected by the abnormality in the time zone after the occurrence time of the abnormality, and the range is set as the influence data range. Select.
  • the influence data range depends on the regional range and time range (time range) in which the abnormality affects, so the influence data is in the regional range in which the abnormality affects the region, and the abnormality is in time. It is a measured value of the time zone (time range) that has an influence on the environment.
  • the occurrence of water leakage is estimated by the DMA 340 after the determination time 480.
  • the sensor having a hydraulic connection relationship with the DMA 340 in the regional range), that is, the flow rate sensor 310-311 and the pressure sensor 320-
  • the measurement value of 322 is selected as the influence data range in which the abnormality affects.
  • the influence data selection unit 111 is hydraulically connected to one of the DMAs 340 and 341. All relevant sensors are selected as influence data ranges as regional ranges.
  • the influence data selection unit 111 excludes a sensor that is not affected by the abnormality determined by the prediction determination unit 131 by controlling the water pipe network from the influence data range. .
  • the influence data selection unit 111 is based on this control information.
  • the pressure sensor 320 determines that it is not affected by the abnormality determined by the prediction determination unit 131.
  • FIG. 5 is a process flowchart of the determination integration unit 112.
  • the determination integration unit 112 starts processing (S501)
  • the abnormality determination result is input from the prediction determination unit 131
  • the influence data range is input from the influence data selection unit 111 (S502).
  • the determination integration unit 112 calculates an initial value of reliability for the abnormality determination result by each prediction determination method input from the prediction determination unit 131 (S503).
  • the reliability is to quantify the reliability of the abnormality determination result by the prediction determination method. For example, the reciprocal of the size of the section (difference between the prediction upper limit value and the prediction lower limit value) in prediction time determination is used. Can do. That is, since the prediction accuracy is poor if the section is large, the reliability is low.
  • the initial value of reliability may be a fixed value for each prediction determination method, stored in advance in the prediction determination method data storage unit 123, and the determination integration unit 112 may read the initial value of reliability.
  • the determination integration unit 112 determines whether there is a result determined to be abnormal among the abnormality determination results by each prediction determination method input from the prediction determination unit 131 (S504).
  • the judgment integration unit 112 proceeds to S505 if there is an abnormality, and proceeds to S506 if there is no abnormality.
  • the determination integration unit 112 determines whether there is an abnormality determination result using the influence data included in the influence data range of the abnormality determination extracted in S504 among the abnormality determination results by each prediction determination method input from the prediction determination unit 131. And the process of lowering the reliability of the abnormality determination result using the influence data is performed. In the process of reducing the reliability, for example, the reliability of the abnormality determination result using the influence data is set to 0. The degree of reliability may be calculated more precisely according to the number of measurement values as influence data used by the prediction determination unit 131 for prediction determination. Further, the determination as to whether or not the influence data is used includes not only the measurement value used for the prediction but also the measurement value used for adjusting the parameter of the prediction determination method.
  • the determination integration unit 112 integrates the abnormality determination result by each prediction determination method input from the prediction determination unit 131 based on the reliability (S506).
  • the integration method for example, the abnormality determination result with the highest reliability is adopted.
  • an integration method may be used, such as taking a majority vote of abnormality determination results having a reliability level equal to or higher than a predetermined value, and averaging the abnormality determination results after weighting with reliability.
  • the determination integration unit 112 outputs the integrated abnormality determination result to the output unit 152 (S507), and ends the process (S508).
  • the prediction determination unit 131 for the measurement value 404 at the determination time 481 determines that the prediction determination result 491 using the prediction determination method 1 is normal and the prediction determination result 492 using the prediction determination method 2 is abnormal. . Since the prediction determination method 1 is determined using the most recent measurement value compared to the prediction determination method 2, the initial value of the reliability is higher in the prediction determination method 1 than in the prediction determination method 2. Is expensive.
  • the prediction determination unit 131 uses a measurement value (influence data) on which the abnormality determined by the prediction determination method 2 temporally affects the determination at the determination time 481 by the prediction determination method 1. For this reason, the determination integration unit 112 sets the reliability of the prediction determination method 1 to 0 (lowers the reliability), and adopts the result of the prediction determination method 2 with the highest reliability as a result.
  • FIG. 6 is a display example of the detection result of the occurrence of water leakage by the abnormality detection device 101. A method in which the output unit 152 presents the abnormality determination result to the operator will be described with reference to FIG.
  • the abnormality detection result window 601 displayed on the display unit 206 such as a display by the output unit 152 includes a prediction determination display panel 602 and a determination integrated display panel 603.
  • the output unit 152 displays the abnormality determination result input from the determination integration unit 112 on each panel of the abnormality detection result window 601.
  • the output unit 152 displays the abnormality determination result on the prediction determination display panel 602.
  • information corresponding to the prediction determination result 492 of FIG. 4 that is the basis for the abnormality determination is displayed.
  • an influence data range that is determined to be abnormal and the abnormality affects temporally is highlighted as an abnormal influence time range 621.
  • the operator operates the sensor selection box 611 and the prediction determination method selection box 612 to change the sensor and prediction determination method displayed on the prediction determination display panel 602 by the output unit 152.
  • the output unit 152 displays the determination result of each prediction determination method and information used for integration on the determination integrated display panel 603.
  • the ID of the prediction judgment method As information used for the integration, the ID of the prediction judgment method, its reliability, the judgment result of normal or abnormal, the presence or absence of use of the influence data, the abnormal part, the occurrence time of the abnormal, and The type is displayed.
  • the abnormality detection device 101 can detect even the occurrence of small-scale water leakage based on the measured values of the sensors such as the flow rate and pressure.
  • an abnormality detection device that does not include a prediction determination unit that performs prediction determination by a plurality of prediction determination methods and an integrated determination unit that integrates determination results, detects the occurrence of small-scale water leakage by increasing the sensitivity of abnormality determination. As a result, misreports frequently occur that determine that a change in the measured value of the sensor within the normal range is abnormal. For this reason, only the occurrence of large-scale water leakage can be detected practically.
  • the abnormality detection device of the present embodiment it is possible to detect even the occurrence of an abnormality such as a small-scale water leak that gives a gradual change in measured values such as flow rate and pressure.
  • the abnormality detection device evaluates the reliability of the prediction determination method based on whether or not the influence data affected by the abnormality is used, and then integrates the determination results to generate a false alarm. It is possible to detect the occurrence of small-scale water leakage by keeping the ratio of low.
  • the abnormality detection device of the present embodiment can detect events that increase gradually and become large-scale water leakage earlier.
  • FIG. 7 is a configuration diagram of the abnormality detection system 100 of the present embodiment.
  • the abnormality detection apparatus 101 includes a method selection unit 701 that selects a prediction determination method used by the prediction determination unit 131 based on the abnormality determination result of the determination integration unit 112. The description will focus on the differences from the first embodiment.
  • the prediction determination unit 131 is selected by the method selection unit 701 among the prediction determination methods stored in the prediction determination method data storage unit 123 (strictly, the prediction determination method determined by the prediction determination method data). Prediction and determination are performed by a prediction determination method.
  • the prediction determination method stored in the prediction determination method data storage unit 123 is referred to as a prediction determination method candidate.
  • the method selection unit 701 inputs the abnormality determination result and the influence data range from the determination integration unit 112.
  • the method selection unit 701 reads the prediction determination method candidate list from the prediction determination method data storage unit 123, refers to the input abnormality determination result and the influence data range, and uses the prediction determination unit to use the prediction determination unit from the read candidate list A method is selected, and the selected prediction determination method is output to the prediction determination unit 131.
  • the prediction determination unit 131 using the prediction determination method 2 does not use the influence data at the determination time 481, but the prediction determination unit 131 uses the prediction determination method 2 when more time elapses. Impact data must be used.
  • the method selection unit 701 measures the data range from which the influence data (measurement value) affected by the abnormality is excluded from the prediction determination method candidates when the determination integration unit 112 determines that the abnormality is present. Select the prediction judgment method that uses the value.
  • the method selection unit 701 selects a prediction determination method that uses a measurement value in the past time range further than the prediction determination method 2.
  • the method selection unit 701 does not use the measurement value of the DMA 340 sensor, and various auxiliary data such as day of the week and weather stored in the auxiliary data storage unit 122, and the water distribution flow rate of the DMA highly correlated with the DMA 340
  • the prediction determination method for predicting the flow rate of the flow rate sensor 311 is selected.
  • the criterion for abnormality determination that serves as a trigger for the method selection unit 701 to select the prediction determination method may be lower in certainty (the probability of the abnormality determination result) than the criterion for abnormality determination presented to the operator. That is, the prediction determination unit 131 provides a two-stage time interval (data range) to obtain a two-stage certainty and determines an abnormality, and the method selection unit 701 predicts using an abnormality determination result with a low certainty as a trigger. A determination method may be selected. In this case, the determination integration unit 112 outputs an abnormality determination result with a high degree of certainty to the output unit 152.
  • the method selection unit 701 selects the prediction determination method when the certainty level is less than a predetermined threshold, and the certainty level is predetermined.
  • the abnormality determination result may be output to the output unit 152 when the threshold value is equal to or greater than the threshold value.
  • the abnormality detection apparatus 101 selects the prediction determination method in which the method selection unit does not use the abnormality influence data, so that the degree of certainty of the abnormality determination result can be increased even when the abnormality continues for a long time. Maintained abnormality detection can be continued.
  • the abnormality detection apparatus 101 of the present embodiment can suppress the processing load of the abnormality detection apparatus 101 by selectively using a prediction determination method that is effective for determining abnormality.
  • the present embodiment is an abnormality detection apparatus 101 and an abnormality detection system 100 in which the method selection unit 701 instructs collection of measurement values.
  • the anomaly detection system 100 includes a sensor 191 that can collect measurement values according to an explicit instruction from the measurement value collection device 102, which corresponds to both periodic operation and demand-based operation, although the measurement value collection cycle in normal times is long. Including.
  • a sensor for example, it is a charge meter of a large-volume water consumer, and the water usage is measured and recorded in a cycle of 30 minutes, but the recorded measurement value is transmitted in a cycle of one day. It is a set smart meter.
  • the anomaly detection apparatus 101 uses the measurement value of the sensor corresponding to the measurement value collection instruction (demand) in the prediction determination method data storage unit 123 as a candidate for the prediction determination method for performing highly reliable prediction determination. As stored.
  • the method selection unit 701 selects the above-described candidate prediction determination method (data), outputs the selected prediction determination method to the prediction determination unit 131, and the selected prediction determination method.
  • the measurement value collection unit 151 is instructed to collect the measurement values required by.
  • the measurement value collection unit 151 instructs the measurement value collection device 102 to collect necessary measurement values.
  • the method selection unit 701 instructs the measurement value collection device 102 to collect the water usage measured by the smart meter, and selects the prediction determination method that uses the water usage measured by the smart meter as the measurement value.
  • use of the water similar to a water leak can be discriminate
  • the anomaly detection apparatus 101 of the present embodiment for example, by collecting the measured values from the smart meter for the water leakage and the water usage of the consumer, it is possible to suppress false reports of the occurrence of water leakage.
  • the prediction determination unit 131 outputs an abnormality determination result at a period according to each feature of the plurality of prediction determination methods.
  • the prediction determination method used by the prediction determination unit 131 includes a prediction determination method for performing prediction and determination based on the nighttime flow rate.
  • the nighttime flow rate is the minimum value of the flow rate observed in a specific time zone at midnight. Prediction / determination based on the nighttime flow rate is highly reliable, but it can be determined only once a day (the determination cycle is one day), and it takes time from the occurrence of water leakage to its detection. The abnormality determination result is also output once a day.
  • the determination integration unit 112 determines that the determination period is long and the reliability of the abnormality determination result when it is determined to be abnormal by the short determination period prediction determination method corresponding to the measurement value collection period as described in the first embodiment. Therefore, the abnormality determination result is integrated so as to select the determination of abnormality without adopting the determination result of the prediction determination method having a high value, that is, the prediction determination method based on the nighttime flow rate.
  • the abnormality detection device 101 of the present embodiment it is possible to detect the occurrence of water leakage with high reliability based on the nighttime flow rate and to detect the occurrence of water leakage in a short time from the occurrence of water leakage.
  • the abnormality detection device for monitoring the water pipe network has been described.
  • the monitoring target is not limited to the water pipe network, and the abnormality detection device can be used for various processes in which abnormality determination based on prediction is effective. It can be applied to anomaly detection.
  • the anomaly detection device sets the measurement items of the sensor (type of sensor) and auxiliary data to be used appropriately so that the water supply process, the water purification process, and the gas supply that supplies resources through the pipe network and pipeline Processes and chemical plant operation processes can be monitored.
  • the auxiliary data may be an operation plan or a control target value in a specific line of the plant, and various prediction judgment methods using these data may be used.
  • 100 Anomaly detection system
  • 101 Anomaly detection device
  • 102 Measurement value collection device
  • 103 Alarm display device
  • 111 Influence data selection unit
  • 112 Determination integration unit
  • 121 Measurement value storage unit
  • 122 Auxiliary data storage Unit
  • 123 prediction determination method data storage unit
  • 151 measurement value collection unit
  • 152 output unit
  • 191 sensor
  • 701 method selection unit.

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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Examining Or Testing Airtightness (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Dans la présente invention, même une anomalie de processus qui occasionne une variation modérée de valeurs mesurées est détectée. Le dispositif de détection d'anomalie selon l'invention comprend une unité de prédiction et d'évaluation, une unité de sélection de données d'effet, une unité d'intégration d'évaluation et une partie de sortie. Dans l'unité de prédiction et d'évaluation : la valeur de prédiction d'une première valeur de mesure d'un capteur dans un processus contrôlé à l'instant où une estimation est réalisée est prédite, au moyen de chacun des procédés de prédiction et d'évaluation d'une pluralité de procédés de prédiction et d'évaluation, sur la base de valeurs de mesure provenant du capteur sur une plage de données correspondant à la pluralité de procédés de prédiction et d'évaluation ; le processus devant être contrôlé est évalué comme étant anormal sur la base (1) de la différence entre la valeur de prédiction et la première valeur de mesure au moment où l'évaluation est réalisée, et/ou (2) de la première valeur de mesure en dehors de la plage entre les limites supérieure et inférieure des valeurs de prédiction ; et les résultats d'évaluation d'anomalie pour lorsqu'une anomalie a été évaluée comme ayant eu lieu au moyen de la pluralité de procédés d'évaluation prédictive sont délivrés. Dans l'unité de sélection de données d'effet, une plage qui comprend, comme données d'effet, une seconde valeur de mesure affectée par l'anomalie comprise dans les résultats d'évaluation d'anomalie est sélectionnée comme la plage de données d'effet. Dans l'unité d'intégration d'évaluation, la fiabilité des résultats d'évaluation d'anomalie qui sont des résultats d'évaluation d'anomalie basés sur les données d'effet comprises dans la plage de données d'effet est réduite, et les résultats d'évaluation d'anomalie de la pluralité de procédés de prédiction et d'évaluation sont intégrés. La partie de sortie délivre les résultats d'évaluation d'anomalie intégrés.
PCT/JP2017/006258 2016-02-29 2017-02-21 Dispositif de détection d'anomalie, système de détection d'anomalie, et procédé correspondant Ceased WO2017150263A1 (fr)

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019124678A (ja) * 2018-01-18 2019-07-25 株式会社日立製作所 作業端末、漏油検出装置、及び、漏油検出方法
WO2019142446A1 (fr) * 2018-01-18 2019-07-25 株式会社日立製作所 Terminal de travail, dispositif de détection de fuite d'huile, et procédé de détection de fuite d'huile
JP2020042692A (ja) * 2018-09-13 2020-03-19 株式会社日立製作所 プラント診断用データ生成システムおよび方法
CN111212950A (zh) * 2017-10-09 2020-05-29 维家技术有限及两合公司 具有饮用水品质监控的饮用水供应系统，其控制方法以及计算机程序
CN113153551A (zh) * 2020-01-07 2021-07-23 丰田自动车株式会社 空气流量计的异常诊断装置
US20220050004A1 (en) * 2020-08-13 2022-02-17 Alarm.Com Incorporated Periodic water leak detection
US11657121B2 (en) 2018-06-14 2023-05-23 Mitsubishi Electric Corporation Abnormality detection device, abnormality detection method and computer readable medium
US12618742B2 (en) 2023-09-18 2026-05-05 Alarm.Com Incorporated Periodic water leak detection

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3074590B1 (fr) * 2017-12-04 2023-03-17 Soc Air France Methode de prediction d'une anomalie de fonctionnement d'un ou plusieurs equipements d'un ensemble
JP7063022B2 (ja) * 2018-03-14 2022-05-09 オムロン株式会社 異常検知システム、サポート装置およびモデル生成方法
JP6678841B2 (ja) * 2018-03-20 2020-04-08 三菱電機株式会社 表示装置、表示システム、および表示画面生成方法
EP3789547A4 (fr) * 2018-06-11 2021-12-08 Hitachi, Ltd. Procédé de détection de fuite d'eau, dispositif de détection de fuite d'eau et terminal capteur de vibrations
JP7452311B2 (ja) * 2020-07-30 2024-03-19 株式会社明電舎 漏水検出装置及び漏水検出方法
JP2024070046A (ja) * 2022-11-10 2024-05-22 伸和コントロールズ株式会社 状態検知装置、状態検知方法及びコンピュータプログラム
CN120649535A (zh) * 2025-06-04 2025-09-16 灵昊智能科技(宁波)有限公司 用于水电气系统的恒压供水系统

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003050631A (ja) * 2001-08-07 2003-02-21 Mitsui Eng & Shipbuild Co Ltd 異常診断システムの学習データ生成方法、異常診断システムの構築プログラム、異常診断プログラム、異常診断システムの構築装置および異常診断システム
JP2013156969A (ja) * 2012-02-01 2013-08-15 Fuji Xerox Co Ltd 障害予測システム、障害予測装置及びプログラム
JP2014518580A (ja) * 2011-04-29 2014-07-31 トヨタ モーター エンジニアリング アンド マニュファクチャリング ノース アメリカ，インコーポレイティド 協働的なマルチエージェント式の車両障害診断システム及び関連する方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4822990B2 (ja) * 2006-09-07 2011-11-24 株式会社東芝 漏水監視システム
JP4612696B2 (ja) * 2008-02-13 2011-01-12 株式会社東芝 配水管路の漏水診断装置及び漏水診断方法
WO2015063931A1 (fr) * 2013-10-31 2015-05-07 株式会社日立製作所 Détecteur de fuite d'eau, système de détection de fuite d'eau et procédé de détection de fuite d'eau
WO2015129031A1 (fr) * 2014-02-28 2015-09-03 株式会社日立製作所 Système et procédé de détection de fuite d'eau

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003050631A (ja) * 2001-08-07 2003-02-21 Mitsui Eng & Shipbuild Co Ltd 異常診断システムの学習データ生成方法、異常診断システムの構築プログラム、異常診断プログラム、異常診断システムの構築装置および異常診断システム
JP2014518580A (ja) * 2011-04-29 2014-07-31 トヨタ モーター エンジニアリング アンド マニュファクチャリング ノース アメリカ，インコーポレイティド 協働的なマルチエージェント式の車両障害診断システム及び関連する方法
JP2013156969A (ja) * 2012-02-01 2013-08-15 Fuji Xerox Co Ltd 障害予測システム、障害予測装置及びプログラム

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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JP2019124678A (ja) * 2018-01-18 2019-07-25 株式会社日立製作所 作業端末、漏油検出装置、及び、漏油検出方法
WO2019142446A1 (fr) * 2018-01-18 2019-07-25 株式会社日立製作所 Terminal de travail, dispositif de détection de fuite d'huile, et procédé de détection de fuite d'huile
TWI714950B (zh) * 2018-01-18 2021-01-01 日商日立製作所股份有限公司 作業終端、漏油檢測裝置及漏油檢測方法
US11494888B2 (en) 2018-01-18 2022-11-08 Hitachi, Ltd. Work terminal, oil leakage detection apparatus, and oil leakage detection method
US11657121B2 (en) 2018-06-14 2023-05-23 Mitsubishi Electric Corporation Abnormality detection device, abnormality detection method and computer readable medium
JP7002427B2 (ja) 2018-09-13 2022-01-20 株式会社日立製作所 プラント診断用データ生成システムおよび方法
JP2020042692A (ja) * 2018-09-13 2020-03-19 株式会社日立製作所 プラント診断用データ生成システムおよび方法
CN113153551A (zh) * 2020-01-07 2021-07-23 丰田自动车株式会社 空气流量计的异常诊断装置
CN113153551B (zh) * 2020-01-07 2023-02-17 丰田自动车株式会社 空气流量计的异常诊断装置
US20220050004A1 (en) * 2020-08-13 2022-02-17 Alarm.Com Incorporated Periodic water leak detection
US11788920B2 (en) * 2020-08-13 2023-10-17 Alarm.Com Incorporated Periodic water leak detection
US12618742B2 (en) 2023-09-18 2026-05-05 Alarm.Com Incorporated Periodic water leak detection

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