Fault prediction method for nuclear power machinery based on Bayesian PPCA recurrent neural network model
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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
Received: 17 April 2020 / Revised: 10 June 2020 / Accepted: 14 June 2020 China Science Publishing & Media Ltd. (Science Press), Shanghai Institute of Applied Physics, the Chinese Academy of Sciences, Chinese Nuclear Society and Springer Nature Singapore Pte Ltd. 2020
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 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. & Jia-Liang Li [email protected] & Dong-Dong You [email protected] 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
proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified. Keywords Fault prediction Nuclear power machinery Steam turbine Recurrent neural network Probabilistic principal component analysis Bayesian confidence
1 Introduction Using a real-time monitoring system to collect the operations data of mechanical equipment in nuclear power plants (NPPs) for early warning in the early stage of equipment failure allows troubleshooting, which avoids major safety accidents, reduces unplanned shutdown maintenance of units, and reduces costs [1–3]. The establishment of a data-driven prediction model for mechanical equipment fault prediction has become an important means of predictive maintenance, and research on nuclear power machinery has gradually increased because of the r
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