Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network
A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/6/1651 |
| _version_ | 1818039556784521216 |
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| author | Jeonghun Choi Seung Jun Lee |
| author_facet | Jeonghun Choi Seung Jun Lee |
| author_sort | Jeonghun Choi |
| collection | DOAJ |
| description | A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states. |
| first_indexed | 2024-12-10T08:00:31Z |
| format | Article |
| id | doaj.art-22b89fed00bf4f98b703b379ce4fdeb4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T08:00:31Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-22b89fed00bf4f98b703b379ce4fdeb42022-12-22T01:56:47ZengMDPI AGSensors1424-82202020-03-01206165110.3390/s20061651s20061651Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM NetworkJeonghun Choi0Seung Jun Lee1Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulju-gun, Ulsan 44919, KoreaUlsan National Institute of Science and Technology, 50 UNIST-gil, Ulju-gun, Ulsan 44919, KoreaA nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states.https://www.mdpi.com/1424-8220/20/6/1651sensor fault detectionconsistency indexmachine learningemergency situationsmisdiagnosis prevention |
| spellingShingle | Jeonghun Choi Seung Jun Lee Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network Sensors sensor fault detection consistency index machine learning emergency situations misdiagnosis prevention |
| title | Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network |
| title_full | Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network |
| title_fullStr | Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network |
| title_full_unstemmed | Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network |
| title_short | Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network |
| title_sort | consistency index based sensor fault detection system for nuclear power plant emergency situations using an lstm network |
| topic | sensor fault detection consistency index machine learning emergency situations misdiagnosis prevention |
| url | https://www.mdpi.com/1424-8220/20/6/1651 |
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