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<p>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.</p>
</abstract>

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.

ECOC-based integrated learning method for fault diagnosis in nuclear power plants

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

ECOC-based integrated learning method for fault diagnosis in nuclear power plants

Published Online: Sep 20, 2023

Page range: -

Received: Oct 10, 2022

Accepted: Mar 31, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00354

Keywords
Nuclear power plant fault diagnosis, Condition monitoring unit, IFWA algorithm, Integrated learning, SGTR-LOCA

© 2023 Guimin Sheng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

ECOC-based integrated learning method for fault diagnosis in nuclear power plants

Published Online: Sep 20, 2023

Page range: -

Received: Oct 10, 2022

Accepted: Mar 31, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00354

KeywordsNuclear power plant fault diagnosis, Condition monitoring unit, IFWA algorithm, Integrated learning, SGTR-LOCA

© 2023 Guimin Sheng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Nuclear power plant fault diagnosis, Condition monitoring unit, IFWA algorithm, Integrated learning, SGTR-LOCA