Research on diagnosis method of centrifugal pump rotor faults based on IPSO-VMD and RVM

Dong Liang; Chen Zeyu; Hua Runan; Hu Siyuan; Fan Chuanhan; Xiao xingxin

doi:10.1016/j.net.2022.10.045

추천

검색

자료유형: 학술저널

저자정보: Dong Liang (National Research Center of Pumps Jiangsu University) Chen Zeyu (National Research Center of Pumps Jiangsu University) Hua Runan (Wuhan Second Ship Design and Research Institute) Hu Siyuan (National Research Center of Pumps Jiangsu University) Fan Chuanhan (National Research Center of Pumps Jiangsu University) Xiao xingxin (National Research Center of Pumps Jiangsu University)

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제55권 제3호

발행연도: 2023.3

수록면: 827 - 838 (12page)

DOI: 10.1016/j.net.2022.10.045

이용수

📌

연구주제

📖

연구배경

🔬

연구방법

🏆

연구결과

초록· 키워드

오류제보하기

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 nonstationary 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%.

#Improved particle swarm algorithm #Variational modal decomposition #Kullback-Leibler divergence value #Relevance vector machine #Centrifugal pump rotor faults

참고문헌 (0)

참고문헌 신청

참고문헌이 DBpia에서 서비스 중이라면, [참고문헌 신청]을 통해 등록해보세요

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!