WO2014091952A1 - Dispositif de surveillance de capteur, procédé de surveillance de capteur et programme de surveillance de capteur - Google Patents

Dispositif de surveillance de capteur, procédé de surveillance de capteur et programme de surveillance de capteur Download PDF

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Publication number
WO2014091952A1
WO2014091952A1 PCT/JP2013/082303 JP2013082303W WO2014091952A1 WO 2014091952 A1 WO2014091952 A1 WO 2014091952A1 JP 2013082303 W JP2013082303 W JP 2013082303W WO 2014091952 A1 WO2014091952 A1 WO 2014091952A1
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WIPO (PCT)
Prior art keywords
sensor
correlation
sensors
values
sets
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PCT/JP2013/082303
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English (en)
Japanese (ja)
Inventor
哲 寺澤
加藤　真也
敬之 山本
敬喜 朝倉
林　司
山本　秀夫
睦男 生田
安達　勝
将弘 崎部
健三 宮
知也 相馬
真弓 高城
大石　敏之
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日本電気株式会社
中国電力株式会社
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Application filed by 日本電気株式会社, 中国電力株式会社 filed Critical 日本電気株式会社
Priority to JP2014551979A priority Critical patent/JP6304767B2/ja
Publication of WO2014091952A1 publication Critical patent/WO2014091952A1/fr

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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
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks

Definitions

  • the present invention relates to a sensor monitoring device, a sensor monitoring method, and a sensor monitoring program for monitoring the state of a sensor, and more particularly to a sensor monitoring device, a sensor monitoring method, and a sensor monitoring program for monitoring a sensor installed in a power plant.
  • various devices are diagnosed and maintenance work is performed when necessary for the purpose of operating safely and efficiently. From the viewpoint of safety, especially in power plants such as nuclear power plants, many sensors are installed, and various types of parameters are constantly measured and diagnosed. Diagnosis of various devices is performed by a plurality of sensors attached to a device such as a water supply pump. Usually, it is understood that some abnormality has occurred in the device when the measured value detected by the sensor exceeds a predetermined threshold. Here, when sensor drift occurs in the sensor due to aging deterioration or environmental change, the measured value deviates from the correct value. That is, when any abnormality is recognized in the measurement value detected by the sensor, it cannot be determined whether an abnormality has occurred in the device or in the sensor itself.
  • An object of the present invention is to provide a sensor monitoring apparatus, a sensor monitoring method, and a sensor monitoring program that solve the above-described misdiagnosis.
  • one embodiment of the present invention is a sensor monitoring device provided in a power plant, which includes a plurality of sensor sets each including a plurality of sensors, and sensors from the plurality of sensors constituting each sensor set.
  • a database for modeling correlation between values and sensor values from the plurality of sensors constituting another sensor set, and storing them as a relationship model, and sensor detection values in the plurality of sensors constituting each sensor set And the correlation between the sensor detection values of the plurality of sensors constituting the other sensor set and the correlation of the modeled relationship model stored in the database and comparing the detected sensor values.
  • a correlation detection module for detecting disturbances from the correlation of the modeled relationship model in the correlation of It relates to a sensor monitoring apparatus characterized by.
  • Another aspect of the present invention is a sensor monitoring method executed at a power plant, wherein sensor values from a plurality of sensors constituting each sensor set and a plurality of sensors constituting another sensor set are received.
  • sensor values from a plurality of sensors constituting each sensor set and a plurality of sensors constituting another sensor set are received.
  • sensor detection values in the plurality of sensors constituting each sensor set and sensors in the plurality of sensors constituting another sensor set The correlation between the detected values is compared with the correlation of the modeled relationship model stored in the database and the correlation of the modeled relationship model in the correlation of the detected sensor value
  • the present invention relates to a sensor monitoring method characterized by detecting disturbance from a relationship.
  • another aspect of the present invention is a sensor monitoring program executed in a power plant, wherein sensor values from a plurality of sensors constituting each sensor set and a plurality of sensors constituting another sensor set are received.
  • a process for referring to a database storing a relationship model in which a correlation with a sensor value is modeled, a sensor detection value in the plurality of sensors constituting each of the sensor sets, and the plurality of sensors constituting another sensor set The process of comparing the correlation between the sensor detection values in FIG. And the correlation of the modeled relationship model stored in the database and the modeled relation in the correlation of the detected sensor values
  • the present invention relates to a sensor monitoring program for causing a computer to execute processing for detecting disturbances from the correlation of models.
  • FIG. 1 is a block diagram showing a schematic configuration of a system according to an embodiment of the present invention.
  • FIG. 2 is a flowchart for explaining a model generation phase for constructing a relationship model executed in one embodiment of the present invention.
  • FIG. 3 is a flowchart for explaining a monitoring phase executed in an embodiment of the present invention.
  • FIG. 4 is a graph showing sensor measurement values of the respective sensors installed at the same location in the related art.
  • FIG. 4 is a graph showing sensor measurement values (detection values) of a plurality of sensors arranged in the same place with time.
  • the sensor measured values by the three sensors # 1, # 2, and #N are graphed.
  • a process value when a process value is measured by a plurality of sensors installed at the same physical location, if the measured value of each of the plurality of sensors is the same, the measured value may be used as a correct value. it can.
  • the problem here is when the measured values of the plurality of sensors are different. This is because it is unknown which sensor's measured value is correct. For example, in the present situation, an intermediate value (measured value of sensor # 2) is adopted, or an appropriate rule of measurement is determined by predicting an appropriate sensor measurement value by judging from an empirical rule which sensor becomes abnormal due to deterioration. It has been done.
  • a process value shows the state quantity showing the state of a power plant, and the detected value measured by the sensor may correspond directly to a process value.
  • FIG. 1 is a diagram showing a schematic configuration of a system (here, a power plant system) to which a sensor monitoring apparatus according to an embodiment of the present invention is applied.
  • the illustrated power plant system is composed of various devices, and power generation using nuclear power or thermal power is performed.
  • various devices of the power plant system are the measurement target 11.
  • the measurement object 11 include devices such as a nuclear reactor containment vessel, a pressurizer, a condenser, power wiring, a boiler, a turbine, and a water supply pump.
  • Sensors S ⁇ b> 1 to S ⁇ b> 3 and other sensor groups 15 are appropriately arranged on these various devices to be measured 11.
  • the plurality of sensors S1 to S3 are attached to various devices of the system as a sensor set, and measure the process values of the system.
  • one sensor set is physically installed at the same cylinder, and is arranged at a physical position different from that of the other sensor group (a plurality of sensor sets) 15.
  • measured values are constantly measured by a large number of sensor sets composed of sensors S1 to S3 multiplexed in order to grasp the state, and are used for actual driving and safety decision making.
  • the sensors S1 to S3 include sensors such as a temperature sensor, a voltage / current sensor, a vibration sensor, a radiation dose sensor, a pressure sensor, a flow rate sensor, and a water level sensor.
  • the number of sensors is not limited to the three illustrated in FIG. 1, and it goes without saying that four or five or more sensors can be installed for safety measures. The same applies to the other sensor groups 15.
  • the sensor set described above constitutes a part of the sensor monitoring apparatus according to the present embodiment.
  • the monitoring module 13 periodically acquires sensor detection values (measurement values) output from the plurality of sensors S1 to S3 installed in the system.
  • the monitoring module 13 stores sensor detection values from the sensors S1 to S3 as sensor information in the sensor value storage database 17 together with IDs, times, physical positions, measurement target information, and the like of the sensors S1 to S3.
  • the monitoring module 13 periodically acquires sensor detection values from other sensor groups 15 and similarly stores them in the sensor value storage database 17.
  • the data stored in the sensor value storage database 17 is input to the relationship verification module 19 (correlation detection module).
  • the relationship verification module 19 verifies the relationship between the sensor data of the plurality of sensor sets including the plurality of sensors S1 to S3 and the relationship between the sensor data of the plurality of sensors S1 to S3. That is, the relationship verification module 19 includes a relationship storage database 21.
  • the relationship storage database 21 includes a plurality of sensors S1 to S1 as well as correlations between sensor data of the plurality of sensors S1 to S3. Relationships between past sensors and sensor sets that represent correlations between sensor data of a plurality of sensor sets including S3 are accumulated. As the relationship between past sensors and sensor sets, it is preferable to use output data of the sensors S1 to S3 and other sensor groups 15 when the system is operating normally.
  • the verification result in the relationship verification module 19 is given to the drift determination module (determination module) 23 to determine sensor drift. If an abnormality is detected as a result of the determination, an alarm or a display device notifies the user.
  • the monitoring module 13, the relationship verification module 19, and the drift determination module 23 are illustrated as hardware, but these modules are actually configured by a program that can be executed by a computer. Can be done.
  • the monitoring module 13, the relationship verification module 19, and the drift determination module 23 are realized by a storage medium that stores programs for executing operations in these modules and a computer (CPU) that executes these programs. it can.
  • the correlation between the sensor detection values in the plurality of sensors constituting each sensor set and the sensor detection values in the plurality of sensors constituting another sensor set is performed by the relationship verification module 19.
  • the relationship and the correlation of the modeled relationship model stored in the relationship storage database 21 are compared, and the disturbance from the correlation of the modeled relationship model in the correlation of the detected sensor values is compared. By detecting it, it is characterized by detecting an abnormality that has occurred at an early stage.
  • the relationship model generation phase is described as being executed by a program that can be executed by a computer, but it can also be realized by using a hardware circuit.
  • a sensor data collection period is defined and a collection period is set (F1-2).
  • sensor data is acquired during the collection period from the sensors S1 to S3 and the other sensor groups 15 in step F1-3.
  • the acquired sensor data is stored in a memory (not shown) included in the relationship verification module 19.
  • step F1-4 the correlation between the sensor data from the plurality of sensors S1 to S3 and other sensor groups 15 is expressed under the control of the verification program for realizing the operation of the relationship verification module 19.
  • a relationship model is built.
  • the constructed relationship model is stored in the relationship storage database 21 in step F1-5, and the creation of the relationship model is terminated.
  • a monitoring phase in which monitoring is performed using the generated relationship model will be described with reference to FIG.
  • the monitoring phase shown in FIG. 3 is performed using the monitoring module 13, the sensor value storage database 17, the relationship verification module 19, the relationship storage database 21, and the drift determination module 23 shown in FIG.
  • the monitoring phase is executed by a program that realizes an operation corresponding to the operation performed by the above-described module, but can also be realized by using a hardware circuit.
  • FIG. 3 the case where the present invention is applied to monitoring of the sensors S1 to S3 will be described.
  • step F3 is started by acquiring sensor detection values (detection data) from the sensors S1 to S3 in the monitoring module 13 (F2-1), and a sensor set including the sensors S1 to S3; Correlation with other sensor sets is confirmed in step F2-2.
  • This operation is performed using the relationship verification module 19 and the relationship storage database 21.
  • the relationship verification module 19 checks whether there is any disturbance in the correlation between the sensor data from each of the sensors S1 to S3 and the other sensor group 15 by correlation analysis. That is, the correlation model constructed using the sensor values obtained from the plurality of sensor sets including the plurality of sensors S1 to S3 and the sensor detection value from the sensor set including the plurality of sensors S1 to S3 are the same correlation. Is determined in step F2-3.
  • correlation analysis using a relationship model automatically generates an invariant model of the entire system (for example, a power plant) from a plurality of observation data (for example, sensor values from a sensor) obtained from a physical system. This refers to monitoring the state of the system (or the sensor itself) by comparing this model with data obtained from an actual physical system (for example, a sensor detection value in the sensor).
  • the correlation is not disturbed (step F2-3: NO)
  • the process returns to step F2-1 and sensor detection values from other sensors are acquired.
  • step F2-3 step F2-3: YES
  • the relationship verification module 19 determines that an abnormality has occurred in the sensor set including the sensors S1 to S3. The process proceeds to step F2-4.
  • step F2-4 the relationship between the sensors S1 to S3 measuring the same location is checked.
  • step F2-5 the correlation of the sensor S1 with the other sensors S2 and S3 is compared and the value of the sensor S1 with a small disturbance is stored. This operation is performed for the number of sensors installed at the same location (step).
  • step F2-7 NO
  • the process returns to steps 2-4 and F2-5, and the sensor S2 is similarly verified.
  • the correlation disturbance with the other sensors S1 and S2 in the last sensor S3 installed in the same place is compared, and a sensor with a small disturbance is stored (F2-5).
  • step F2-8 After the sensor S3 that measures the same location is verified (F2-7: YES), the process proceeds to step F2-8, and the value of the sensor with the smallest disturbance among the verified sensors S1 to S3 is applied. The process ends.
  • the reason why the value of the sensor having the smallest disturbance among the verified sensors S1 to S3 is applied is that the value of the sensor having the smallest disturbance is the most detected value of the sensors S1 to S3 installed at the same location. This is because the reliability is high.
  • the measured process value is valid or the sensor drift is detected. It is determined whether.
  • the measured value is corrected using the drift estimation value, which is a related technology, and it is possible to shift to inspection based on state standards instead of periodic inspection It becomes.
  • the technology using the correlation analysis based on the relationship model used in the present embodiment the more the number of sensors installed, the more effective analysis can be performed, so in an existing power plant where a large number of sensors are arranged. A very effective analysis can be performed.
  • the present invention is particularly effective for grasping pressure abnormalities that affect a wide area in space and the situation by sensors that are spatially densely installed.
  • an unhealthy state before an abnormality occurs is detected at an early stage, rather than a method of detecting an abnormality for the first time when a threshold is exceeded as in the current abnormality detection means. be able to.
  • abnormality is diagnosed based on the disorder of correlation between the sensor to be audited and thousands of other sensors, it is possible to easily distinguish between abnormality of the device and sensor drift.
  • the sensor monitoring program using the characteristics included in the above-described embodiment is also included in the category of the present invention.
  • the processing of the embodiments may be executed by a computer-readable storage medium encoded with a program, software, or instructions that can be executed by a computer.
  • the storage medium includes not only a portable recording medium such as an optical disk, a floppy (registered trademark) disk, and a hard disk, but also a transmission medium that temporarily records and holds data such as a network.
  • the database is divided into two databases, the sensor value storage database 17 and the relationship storage database 21.
  • the sensor monitoring device, the sensor monitoring method, and the sensor monitoring program of the present invention can be configured by using the sensor value storage database 17 and the relationship storage database 21 as a single database.
  • the sensor monitoring apparatus, the sensor monitoring method, and the sensor monitoring program described in the above-described embodiments can be arbitrarily applied to a power plant such as a nuclear power plant or a thermal power plant.
  • a power plant such as a nuclear power plant or a thermal power plant.
  • the present invention has been described above with reference to the embodiments, but the present invention is not limited to the above embodiments.
  • Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.
  • the present invention can be implemented in various other forms without departing from the spirit or main features thereof. Therefore, the above-mentioned embodiment is only a mere illustration in all points, and should not be interpreted limitedly.
  • the scope of the present invention is indicated by the scope of the claims, and is not restricted by the text of the specification.

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Abstract

La présente invention porte sur une technologie qui surveille des capteurs eux-mêmes et empêche une erreur de diagnostic due à une détérioration au cours du temps et des changements environnementaux. Le dispositif de surveillance de capteur fourni dans des centrales électriques comprend : une pluralité d'ensembles de capteurs ayant chacun une pluralité de capteurs ; une base de données qui modélise la corrélation entre des valeurs de capteur provenant de la pluralité de capteurs configurant chaque ensemble de capteurs et les valeurs de capteur provenant d'une pluralité de capteurs configurant d'autres ensembles de capteurs, et stocke celle-ci en tant que modèle de relation ; et un module de détection de corrélation. Le module de détection de corrélation : compare la corrélation entre les valeurs de détection du capteur pour la pluralité de capteurs configurant chaque ensemble de capteurs et les valeurs de détection de capteur pour la pluralité de capteurs configurant les autres ensembles de capteurs, et la corrélation au modèle de relation modélisé stocké dans la base de données, et détecte une déviation de la corrélation au modèle de relation modélisé, dans la corrélation entre les valeurs de capteur détectées.
PCT/JP2013/082303 2012-12-14 2013-11-25 Dispositif de surveillance de capteur, procédé de surveillance de capteur et programme de surveillance de capteur WO2014091952A1 (fr)

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WO2016147656A1 (fr) * 2015-03-16 2016-09-22 日本電気株式会社 Dispositif de traitement d'informations, procédé de traitement d'informations et support d'enregistrement
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JP2019105920A (ja) * 2017-12-11 2019-06-27 株式会社東芝 プラント点検装置、方法及びプログラム
KR20190075628A (ko) * 2017-12-21 2019-07-01 주식회사 스마트시티그리드 대기 환경 센서의 고장을 검출하기 위한 장치
CN111290368A (zh) * 2014-09-23 2020-06-16 英飞凌科技股份有限公司 使用安全机制的传感器系统
JP2020140278A (ja) * 2019-02-27 2020-09-03 日立Ｇｅニュークリア・エナジー株式会社 異常原因推定方法、および、異常原因推定装置
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KR20190075628A (ko) * 2017-12-21 2019-07-01 주식회사 스마트시티그리드 대기 환경 센서의 고장을 검출하기 위한 장치
KR102046503B1 (ko) * 2017-12-21 2019-11-20 주식회사 스마트시티그리드 대기 환경 센서의 고장을 검출하기 위한 장치
WO2020017702A1 (fr) * 2018-07-20 2020-01-23 주식회사 엠앤디 Procédé de calcul d'incertitude de modèle basé sur des données
KR101918949B1 (ko) 2018-07-20 2018-11-15 주식회사 엠앤디 데이터 기반 모델의 불확도 산출 방법
JP2020140278A (ja) * 2019-02-27 2020-09-03 日立Ｇｅニュークリア・エナジー株式会社 異常原因推定方法、および、異常原因推定装置
JP7153585B2 (ja) 2019-02-27 2022-10-14 日立Ｇｅニュークリア・エナジー株式会社 異常原因推定方法、および、異常原因推定装置
WO2023135876A1 (fr) * 2022-01-17 2023-07-20 日立Ｇｅニュークリア・エナジー株式会社 Système et procédé d'évaluation d'imprécisions d'instruments de mesure

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