Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Research on diagnosis method of centrifugal pump rotor faults based on IPSO-VMD and RVM

  • Liang Dong (National Research Center of Pumps, Jiangsu University) ;
  • Zeyu Chen (National Research Center of Pumps, Jiangsu University) ;
  • Runan Hua (Wuhan Second Ship Design and Research Institute) ;
  • Siyuan Hu (National Research Center of Pumps, Jiangsu University) ;
  • Chuanhan Fan (National Research Center of Pumps, Jiangsu University) ;
  • xingxin Xiao (National Research Center of Pumps, Jiangsu University)
  • Received : 2022.05.27
  • Accepted : 2022.10.30
  • Published : 2023.03.25
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Abstract

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

Keywords

Acknowledgement

This work was supported by National Natural Science Foundation of China (No. 51879122, 52279087), Zhenjiang key research and development plan (GY2017001, GY2018025), the Open Research Subject of Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University (szjj2017094, szjj2016068), -Outstanding Young backbone Teacher, Program Development of Jiangsu Higher Education Institutions (PAPD), and Jiangsu top six talent summit project (GDZB-017).

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