Research on Sensor Fault Diagnosis of Nuclear Power Plant Based on Improved CWT-CNN

Deng Zhiguang; Li Zhengxi; He Liang; Wu Qian; Zhu Jialiang; Zhu Biwei; Xu Tao; Wang Hailin

doi:10.13832/j.jnpe.2024.S2.0156

Volume 45 Issue S2

Jan. 2025

Turn off MathJax

Article Contents

Article Navigation > Nuclear Power Engineering > 2024 > 45(S2): 156-162

Deng Zhiguang, Li Zhengxi, He Liang, Wu Qian, Zhu Jialiang, Zhu Biwei, Xu Tao, Wang Hailin. Research on Sensor Fault Diagnosis of Nuclear Power Plant Based on Improved CWT-CNN[J]. Nuclear Power Engineering, 2024, 45(S2): 156-162. doi: 10.13832/j.jnpe.2024.S2.0156

Citation:

Deng Zhiguang, Li Zhengxi, He Liang, Wu Qian, Zhu Jialiang, Zhu Biwei, Xu Tao, Wang Hailin. Research on Sensor Fault Diagnosis of Nuclear Power Plant Based on Improved CWT-CNN[J]. Nuclear Power Engineering, 2024, 45(S2): 156-162. doi: 10.13832/j.jnpe.2024.S2.0156

Citation:

Deng Zhiguang, Li Zhengxi, He Liang, Wu Qian, Zhu Jialiang, Zhu Biwei, Xu Tao, Wang Hailin. Research on Sensor Fault Diagnosis of Nuclear Power Plant Based on Improved CWT-CNN[J]. Nuclear Power Engineering, 2024, 45(S2): 156-162. doi: 10.13832/j.jnpe.2024.S2.0156

PDF( 24617 KB)

Research on Sensor Fault Diagnosis of Nuclear Power Plant Based on Improved CWT-CNN

doi: 10.13832/j.jnpe.2024.S2.0156

National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2024-06-20
Rev Recd Date: 2024-08-13
Publish Date: 2025-01-06

Abstract

Abstract

The consequences of sensor faults in nuclear power plants are serious, and the inherent complexity of primary circuit system and equipment in nuclear power plants brings difficulties to sensor fault diagnosis based on accurate mathematical models. In this paper, an intelligent sensor fault diagnosis method for nuclear power plant based on deep learning algorithm and time-frequency analysis is proposed, which transforms the signal recognition problem into image recognition problem. Firstly, the continuous wavelet transform (CWT) is used to process the time series signals of seven common health states of typical sensors in nuclear power plants to generate a time-frequency diagram that captures the characteristics of fault signals. Then, the convolutional neural network (CNN) model improved by channel attention mechanism (CA) is trained with pre-processed and labeled data sets, and the subtle image features of the time-frequency diagram are extracted. Based on these features, sensor faults are identified and isolated. This method does not need to model and design thresholds, and it has strong robustness and an accuracy rate of more than 97%. By comparing the diagnostic effects of typical deep learning networks such as long short-term memory network (LSTM) and one-dimensional convolutional neural network (1D-CNN), the effectiveness and superiority of the improved CWT-CNN are verified.
- Nuclear power plant,
- Sensor,
- Fault diagnosis,
- Time-frequency diagram,
- Convolutional neural network (CNN),
- Channel attention mechanism (CA)

FullText(HTML)

References(15)

References

[1]	YANG Z X, XU P, ZHANG B, et al. Nuclear power plant sensor signal reconstruction based on deep learning methods[J]. Annals of Nuclear Energy, 2022, 167: 108765. doi: 10.1016/j.anucene.2021.108765
[2]	GAUTAM S, TAMBOLI P K, ROY K, et al. Sensors incipient fault detection and isolation of nuclear power plant using extended Kalman filter and Kullback–Leibler divergence[J]. ISA Transactions, 2019, 92: 180-190. doi: 10.1016/j.isatra.2019.02.011
[3]	刘红艳,麦艳红,孔繁镍,等. 利用自适应粒子滤波的传感器故障诊断识别[J]. 控制工程,2019, 26(7): 1425-1430.
[4]	YU Y, PENG M J, WANG H, et al. Improved PCA model for multiple fault detection, isolation and reconstruction of sensors in nuclear power plant[J]. Annals of Nuclear Energy, 2020, 148: 107662. doi: 10.1016/j.anucene.2020.107662
[5]	ZHU S M, XIA H, ANNOR-NYARKO M, et al. A robust strategy for sensor fault detection in nuclear power plants based on principal component analysis[J]. Annals of Nuclear Energy, 2021, 164: 108621. doi: 10.1016/j.anucene.2021.108621
[6]	MESSAI A, MELLIT A, ABDELLANI I, et al. On-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs[J]. Progress in Nuclear Energy, 2015, 79: 8-21. doi: 10.1016/j.pnucene.2014.10.013
[7]	尹子会,孟延辉,赵智龙,等. 声振信号构造CNN特征矩阵的断路器储能机构故障诊断方法[J]. 高压电器,2023, 59(9): 242-249.
[8]	马立玲,郭建,汪首坤,等. 基于改进CNN-GRU网络的多源传感器故障诊断方法[J]. 北京理工大学学报,2021, 41(12): 1245-1252.
[9]	VERSTRAETE D, FERRADA A, LÓPEZ DROGUETT E, et al. Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings[J]. Shock and Vibration, 2017, 2017: 5067651.
[10]	ALASKAR H. Deep learning-based model architecture for time-frequency images analysis[J]. International Journal of Advanced Computer Science and Applications, 2018, 9(12): 486-494.
[11]	CHEN R X, HUANG X, YANG L X, et al. Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform[J]. Computers in Industry, 2019, 106: 48-59. doi: 10.1016/j.compind.2018.11.003
[12]	郑攀,周建华,高素杰,等. 基于SPS与CNN的行星齿轮箱故障特征提取与诊断研究[J]. 机械传动,2022, 46(4): 73-79,147.
[13]	郭庆华. CNN在单帧舰船回转运动图像特征提取中的应用[J]. 舰船科学技术,2021, 43(22): 190-192.
[14]	JING L Y, ZHAO M, LI P, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox[J]. Measurement, 2017, 111: 1-10. doi: 10.1016/j.measurement.2017.07.017
[15]	郭旭,骆勇鹏,王林堃,等. 基于CNN与DCGAN的结构振动监测传感器故障诊断及监测数据恢复[J]. 铁道科学与工程学报,2022, 19(11): 3383-3395.