On-line abnormal detection of nuclear power plant sensors based on Kullback-Leibler divergence and ConvLSTM

Author:

Wang Weiding,Yu Junhui,Xu Tao,Zhao ChengpingORCID,Zhou Xinzhi

Funder

Nuclear Power Institute of China

Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China

Publisher

Elsevier BV

Reference22 articles.

1. Baier, L., Schlör, T., Schöffer, J., Kühl, N., 2021. Detecting concept drift with neural network model uncertainty. arXiv preprint arXiv:2107.01873.

2. Davis, E., Funk, D., Hooten, D., Rusaw, R., 1998. On-Line Monitoring of Instrument Channel Performance, EPRI, Palo Alto, CA: 1998. TR-104965.

3. Research on sensor fault diagnosis in nuclear power plants based on improved LSTM;Deng;Process Autom. Instrum.,2023

4. Hashemian, H., Rasmussen, B., 2006a. Plant Application of On-Line Monitoring for Calibration Interval Extension of Safety-Related Instruments: Volume 1. EPRI, Palo Alto, CA, and British Energy Group PLC, Suffolk, UK: 2006. 1013486.

5. Hashemian, H., Rasmussen, B., 2006b. Plant Application of On-Line Monitoring for Calibration Interval Extension of Safety-Related Instruments: Volume 2, EPRI, Palo Alto, CA, and British Energy Group PLC, Suffolk, UK: 2006. 1013486.

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