Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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A multi-layer approach to DN 50 electric valve fault diagnosis using shallow-deep intelligent models

  • Liu, Yong-kuo (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment) ;
  • Zhou, Wen (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Ayodeji, Abiodun (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Zhou, Xin-qiu (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Peng, Min-jun (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Chao, Nan (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University)
  • Received : 2020.01.09
  • Accepted : 2020.07.03
  • Published : 2021.01.25
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Abstract

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

Keywords

Acknowledgement

This paper is funded by the Project for State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment (No.K-A2019.418), the Technical Support Project for Suzhou Nuclear Power Research Institute (SNPI, No.029-GN-b-2018-C45-P.0.99-00003), The Basic Research Project (No. JCKY2017xx7B019) and the Foundation of Science and Technology on Reactor System Design Laboratory (No. HT-KFKT-14-2017003).

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