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

• Published in: Nuclear science and techniques Vol. 31; no. 8; pp. 13 - 23
• Main Authors: Ling, Jun, Liu, Gao-Jun, Li, Jia-Liang, Shen, Xiao-Cheng, You, Dong-Dong
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.08.2020; Department of Automation, Shanghai Jiao Tong University,800 Dongchuan Road, Shanghai 200240, China%State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518172, China%School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China; State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518172, China
• Subjects: Energy; Hadrons; Heavy Ions; Nuclear Energy; Nuclear Physics; Steam turbine; Fault prediction; Recurrent neural network; Probabilistic principal component analysis; Bayesian confidence; Nuclear power machinery
• ISSN: 1001-8042; 2210-3147
• DOI: 10.1007/s41365-020-00792-9

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.