A novel method of composite multiscale weighted permutation entropy and machine learning for fault complex system fault diagnosis

Abstract

A novel fault diagnosis method is proposed for rolling bearing by combining extreme-point symmetric mode decomposition (ESMD) composite multiscale weighted permutation entropy (CMWPE) and gravitational search algorithm based on multiple adaptive constraint strategy (MACGSA) optimized least squares support vector machine (LSSVM). In order to solve the problem of intrinsic mode function (IMF) modal aliasing and small differences in fault features, ESMD and CMWPE are used to obtain a more sensitive high-dimensional feature vector set. Aiming at the low accuracy of LSSVM fault diagnosis, MACGSA was used to optimize LSSVM to improve the accuracy of fault diagnosis. ESMD is used to process the rolling bearing data to obtain a series of IMFs; Then, extracting the CMWPE values of IMFs to form a high-dimensional feature vector set; Finally, the MACGSA-LSSVM model is adopted to achieve fault classification. Compared with other diagnostic methods, this method has higher diagnostic accuracy.