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Early fault diagnosis method of rolling bearing based on ISSA-VMD
Published:2023-10-30 author:LIU Yuming, LIU Ziran, WANG Pengbo. Browse: 1003 Check PDF documents
Early fault diagnosis method of rolling bearing based on ISSA-VMD


LIU Yuming, LIU Ziran, WANG Pengbo

(School of Mechanical and Electrical Engineering, Henan University of 
Technology, Zhengzhou 450001, China)


Abstract:  In order to solve the problems that the early signal of rolling bearing was weak which made it difficult to extract fault features, and the accuracy of fault diagnosis was low, a feature extraction method based on improved sparrow search algorithm-variational modal decomposition (ISSA-VMD) and sample entropy(SE) was proposed. First of all, in the process of bearing early fault diagnosis, the choice of the number of modal decomposition and penalty factor had a great influence on the decomposition effect of VMD; in order to eliminate the influence of artificial selection of parameters, sparrow search algorithm (SSA) was optimized to improved sparrow search algorithm (ISSA). The signals were decomposed by the VMD after the parameter optimized by ISSA. Then, the sample entropy of the sensitive intrinsic mode function (IMF) components was calculated to form the eigenvector. Finally, the feature vector was used as the input of support vector machine (SVM) for fault type identification of early fault of rolling bearing. The experimental results show that the fault diagnosis accuracy of ISSA-VMD + sample entropy feature extraction model is 98.3%. Comparing with SSA-VMD + sample entropy, grey wolf optimizer (GWO)-VMD + sample entropy, whale optimization algorithm (WOA)-VMD + sample entropy, traditional VMD + sample entropy and empirical mode decomposition(EMD) + sample entropy feature extraction models, the fault diagnosis accuracy of the model is respectively improved by 3.3%, 6.6%, 5%, 3.3% and 5%. The model can accurately extract fault features and improve the fault diagnosis accuracy.

Key words: early fault of rolling bearing; fault feature extraction; improved sparrow search algorithm-variational modal decomposition(ISSA-VMD); sample entropy(SE); support vector machine(SVM); empirical mode decomposition(EMD)
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