Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2020, Vol. 31 ›› Issue (8): 75 doi: 10.1007/s41365-020-00792-9

• NUCLEAR ENERGY SCIENCE AND ENGINEERING • Previous Articles     Next Articles

Fault prediction method for nuclear power machinery based on Bayesian PPCA recurrent neural network model

Jun Ling1,3 • Gao-Jun Liu1 • Jia-Liang Li2 • Xiao-Cheng Shen2 • Dong-Dong You2   

  1. 1 State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518172, China
    2 School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
    3 Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
  • Received:2020-04-17 Revised:2020-06-10 Accepted:2020-06-14
  • Contact: Dong-Dong You E-mail:youdd@scut.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (No. 51875209), the Guangdong Basic and Applied Basic Research Foundation (No. 2019B1515120060), and the Open Funds of State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment.
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Jun Ling, Gao-Jun Liu, Jia-Liang Li, Xiao-Cheng Shen, Dong-Dong You. Fault prediction method for nuclear power machinery based on Bayesian PPCA recurrent neural network model.Nuclear Science and Techniques, 2020, 31(8): 75     doi: 10.1007/s41365-020-00792-9

Abstract: Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis (PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series. After denoising the source data, the Bayesian PPCA method is employed for dimensional reduction to obtain a refined data group. A recurrent neural network (RNN) prediction model is constructed, and a Bayesian statistical inference approach is developed to quantitatively assess the prediction reliability of the model. By modeling and analyzing the data collected on the steam turbine and components of a nuclear power plant, the results of the goodness of fit, mean square error distribution, and Bayesian confidence indicate that the proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified.

Key words: Fault prediction, Nuclear power machinery, Steam turbine, Recurrent neural network, Probabilistic principal component analysis, Bayesian confidence