Fault Detection for Reactor Coolant Pump Based on Moving Window Kernel Principal Component Analysis

Zhang Xiuchun; Xia Hong; Liu Yongkang; Zhu Shaomin; Liu Jie; Zhang Jiyu

doi:10.13832/j.jnpe.2024.03.0234

Volume 45 Issue 3

Jun. 2024

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Article Navigation > Nuclear Power Engineering > 2024 > 45(3): 234-240

Zhang Xiuchun, Xia Hong, Liu Yongkang, Zhu Shaomin, Liu Jie, Zhang Jiyu. Fault Detection for Reactor Coolant Pump Based on Moving Window Kernel Principal Component Analysis[J]. Nuclear Power Engineering, 2024, 45(3): 234-240. doi: 10.13832/j.jnpe.2024.03.0234

Citation:

Zhang Xiuchun, Xia Hong, Liu Yongkang, Zhu Shaomin, Liu Jie, Zhang Jiyu. Fault Detection for Reactor Coolant Pump Based on Moving Window Kernel Principal Component Analysis[J]. Nuclear Power Engineering, 2024, 45(3): 234-240. doi: 10.13832/j.jnpe.2024.03.0234

Citation:

Zhang Xiuchun, Xia Hong, Liu Yongkang, Zhu Shaomin, Liu Jie, Zhang Jiyu. Fault Detection for Reactor Coolant Pump Based on Moving Window Kernel Principal Component Analysis[J]. Nuclear Power Engineering, 2024, 45(3): 234-240. doi: 10.13832/j.jnpe.2024.03.0234

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Fault Detection for Reactor Coolant Pump Based on Moving Window Kernel Principal Component Analysis

doi: 10.13832/j.jnpe.2024.03.0234

Zhang Xiuchun^{1, 2, 3, 4
,},
Xia Hong^{1, 2
,
,},
Liu Yongkang⁵,
Zhu Shaomin^{1, 2},
Liu Jie^{3, 4},
Zhang Jiyu^{1, 2}

1.
Key Laboratory of Nuclear Safety and Advanced Nuclear Energy Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, 150001, China
2.
Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin, 150001, China
3.
Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, 215000, China
4.
National Engineering Research Center for Nuclear Power Plant Safety & Reliability, Suzhou, Jiangsu, 215000, China
5.
China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong, 518000, China

Received Date: 2023-06-28
Rev Recd Date: 2024-03-15
Publish Date: 2024-06-13

Abstract

Abstract

Due to the influence of component performance decline and operation condition change, nuclear power plants (NPPs) show obvious time variability during operation, which leads to the failure of fault detection model. In order to improve the performance and in-service adaptability of traditional fault detection methods in time-varying industrial processes, this paper proposes a long-term fault detection strategy for NPPs based on kernel principal component analysis (KPCA) and moving window. In this method, the KPCA fault detection model is automatically updated by moving window technology, which solves the time-varying problem of signals in the detection process. The moving window KPCA method is applied to the long-term monitoring of the reactor coolant pump in a nuclear power plant. The results show that the moving window KPCA method has good performance in fault detection rate and false alarm rate under normal and abnormal conditions.
- Nuclear power plant,
- Kernel principal component analysis (KPCA),
- Moving window,
- Time-varying process,
- Fault detection

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References(13)

References

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