ECOC-based integrated learning method for fault diagnosis in nuclear power plants
The fault diagnosis system of nuclear power plants plays an important role in ensuring the safety and economy of nuclear power plant operations. This paper first analyzes typical faults of nuclear power plants and their phenomena, and fault samples are obtained. A comprehensive study of the structur...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
|
| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.00354 |
| _version_ | 1797340785166778368 |
|---|---|
| author | Sheng Guimin Mu Yu Zhang Boyang |
| author_facet | Sheng Guimin Mu Yu Zhang Boyang |
| author_sort | Sheng Guimin |
| collection | DOAJ |
| description | The fault diagnosis system of nuclear power plants plays an important role in ensuring the safety and economy of nuclear power plant operations. This paper first analyzes typical faults of nuclear power plants and their phenomena, and fault samples are obtained. A comprehensive study of the structure of the nuclear power plant system, its working mode and the association between each subsystem is carried out to analyze the monitoring parameters and fault characteristics and establish the fault data set. Secondly, an IFWA (Improved Fireworks Algorithm - Integrated Learning) algorithm is proposed to assess the severity of faults in the first circuit of a nuclear power plant. Finally, the fault diagnosis module is divided into three units according to the functional logic, i.e., condition monitoring unit, fault identification unit, and fault severity assessment unit. The results show that the diagnostic accuracy of the IFWA algorithm is 94.25% for SGTR in the single-fault diagnosis experiment and 96.25% for SGTR-LOCA in the multiple-fault diagnosis experiment. It shows that the IFWA algorithm proposed in this paper has the optimal performance capability when applied to nuclear power plant fault diagnosis and effectively assists managers in diagnosing faults and giving maintenance recommendations. |
| first_indexed | 2024-03-08T10:09:22Z |
| format | Article |
| id | doaj.art-fe42131fe0a0445e8e9e543cf79d81c6 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:09:22Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-fe42131fe0a0445e8e9e543cf79d81c62024-01-29T08:52:31ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00354ECOC-based integrated learning method for fault diagnosis in nuclear power plantsSheng Guimin0Mu Yu1Zhang Boyang22Suihua University, Suihua, Heilongjiang, 152061, China.1Heilongjiang University, Harbin, Heilongjiang, 150080, China.2Suihua University, Suihua, Heilongjiang, 152061, China.The fault diagnosis system of nuclear power plants plays an important role in ensuring the safety and economy of nuclear power plant operations. This paper first analyzes typical faults of nuclear power plants and their phenomena, and fault samples are obtained. A comprehensive study of the structure of the nuclear power plant system, its working mode and the association between each subsystem is carried out to analyze the monitoring parameters and fault characteristics and establish the fault data set. Secondly, an IFWA (Improved Fireworks Algorithm - Integrated Learning) algorithm is proposed to assess the severity of faults in the first circuit of a nuclear power plant. Finally, the fault diagnosis module is divided into three units according to the functional logic, i.e., condition monitoring unit, fault identification unit, and fault severity assessment unit. The results show that the diagnostic accuracy of the IFWA algorithm is 94.25% for SGTR in the single-fault diagnosis experiment and 96.25% for SGTR-LOCA in the multiple-fault diagnosis experiment. It shows that the IFWA algorithm proposed in this paper has the optimal performance capability when applied to nuclear power plant fault diagnosis and effectively assists managers in diagnosing faults and giving maintenance recommendations.https://doi.org/10.2478/amns.2023.2.00354nuclear power plant fault diagnosiscondition monitoring unitifwa algorithmintegrated learningsgtr-loca78-02 |
| spellingShingle | Sheng Guimin Mu Yu Zhang Boyang ECOC-based integrated learning method for fault diagnosis in nuclear power plants Applied Mathematics and Nonlinear Sciences nuclear power plant fault diagnosis condition monitoring unit ifwa algorithm integrated learning sgtr-loca 78-02 |
| title | ECOC-based integrated learning method for fault diagnosis in nuclear power plants |
| title_full | ECOC-based integrated learning method for fault diagnosis in nuclear power plants |
| title_fullStr | ECOC-based integrated learning method for fault diagnosis in nuclear power plants |
| title_full_unstemmed | ECOC-based integrated learning method for fault diagnosis in nuclear power plants |
| title_short | ECOC-based integrated learning method for fault diagnosis in nuclear power plants |
| title_sort | ecoc based integrated learning method for fault diagnosis in nuclear power plants |
| topic | nuclear power plant fault diagnosis condition monitoring unit ifwa algorithm integrated learning sgtr-loca 78-02 |
| url | https://doi.org/10.2478/amns.2023.2.00354 |
| work_keys_str_mv | AT shengguimin ecocbasedintegratedlearningmethodforfaultdiagnosisinnuclearpowerplants AT muyu ecocbasedintegratedlearningmethodforfaultdiagnosisinnuclearpowerplants AT zhangboyang ecocbasedintegratedlearningmethodforfaultdiagnosisinnuclearpowerplants |