Bearing Fault Diagnosis Based on Optimized VMD and Envelope Kurtosis
LIU Feng1,CHEN Xuejun2,ZHANG Lei1,YANG Kang3
1. College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350108, China
2. Fujian Key Laboratory of New Energy Equipment Testing, Putian University, Putian, Fujian 351100, China
3. College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, China
Abstract：In view of the difficulty in selecting the decomposition layer K and penalty factor α of variational mode decomposition (VMD), a subtraction-average-based optimizer (SABO) is proposed to optimize the parameters. Firstly, the SABO is used to optimize K and α, output the optimal parameter combination, and substitute it into VMD to decompose the original vibration signal into K modal components. Then, the maximum envelope kurtosis is used as the index to extract the component with the largest kurtosis among the K modal components as the optimal component, and the eigenvector sample set is constructed by calculating the relevant time-domain and entropy theory characteristic parameters of the optimal component. Finally, the eigenvector sample set is input into the support vector machine (SVM) with mesh search and 5-fold cross-validation for fault diagnosis. To verify the effectiveness of this method, experiments were conducted using the bearing dataset from Case Western Reserve University. The experimental results show that the classification effect of the method is better, and the accuracy rate is 99.44%. Based on the bearing data set experiments of three different working conditions in Jiangnan University, the final fault diagnosis accuracy rate reaches more than 95%.
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2024, Vol. 45 
