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Damage detection of centrifugal pump based on IRCMMDE with acoustic vibration signal fusion

Published:2023-08-14 author:LU Chun-yuan, JIAO Hong-yu. Browse: 390 Check PDF documents

Damage detection of centrifugal pump based on IRCMMDE

with acoustic vibration signal fusion

LU Chun-yuan1, JIAO Hong-yu2

(1.School of Mechanical and Electrical Engineering, Suzhou Vocational University, Suzhou 215104, China;

2.School of Automotive Engineering, Changshu Institute of Technology, Suzhou 215500, China)

Abstract: In order to solve the problem that the early damage characteristics of centrifugal pumps were weak and difficult to extract effectively, an improved refined composite multivariate multiscale dispersion entropy (IRCMMDE)and grey wolf optimizer(GWO)- support vector machine(SVM)method based on acoustic vibration signal fusion was proposed. Firstly, the acoustic and vibration signals of the centrifugal pump under different damage conditions were collected by using multiple sensors and fused to make full use of the damage feature information contained in different types of signals. Secondly, in view of the unstable defect of multivariate multiscale dispersion entropy (MMDE), the coarse granulation processing of MMDE was optimized, and the complexity measurement index of improved refined composite multivariate multiscale dispersion entropy (IRCMMDE) was proposed. Then, the IRCMMDE was used to extract the damage features of the acoustic vibration fusion signal, and the feature matrix of each damage state was constructed. Finally, the support vector machine classifier optimized by gray wolf algorithm was used to recognize the feature matrix, and the final damage detection conclusion was obtained. The research results show that the damage detection scheme based on acoustic vibration signal fusion can achieve the highest fault identification accuracy of 99.2%. Comparing with the methods based on MMDE and RCMMDE, it can identify the damage of centrifugal pumps more accurately. This method also effectively alleviates the uncertainty of single signal detection, and it has high detection accuracy in many experiments.

Key words: acoustic vibration signal fusion; damage detection of centrifugal pump; improved refined composite multivariate multiscale dispersion entropy(IRCMMDE); grey wolf optimizer(GWO); support vector machine(SVM)

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