A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing

Abstract

A novel classification method based on ICGOA-KELM is presented in this paper. In ICGOA-KELM, an improved circle chaotic map with grasshopper optimization algorithm (ICGOA) is designed to optimize the parameters of Kernel extreme learning machine (KELM) to improve the stability and accuracy of fault classification for rolling bearing based on parameter modification of circle chaotic map. Grasshopper optimization algorithm (GOA) is a new heuristic optimization algorithm, which has strong global searching ability. However, it still may fall into local optimization in some cases. In this paper, the vibration signals of rolling bearing are preprocessed by using Variational Modal Decomposition (VMD). Then Multi-scale Permutation Entropy (MPE) is utilized to extracted features of intrinsic mode functions (IMFs) decomposed by VMD. In addition, KPCA is adopted to select the salient features with high contribution rates to remove redundant and irrelevant features. Finally, the salient features are fed into ICGOA-KELM to fulfill fault classification. Therefore, a new fault detection and classification method based on VMD, MPE, KPCA and ICGOA-KELM is proposed. This method is applied to the fault classification of rolling bearing and the identification of different damage fault degrees. Experiments verify that the proposed method is more effective than CGOA-KELM for fault diagnosis of rolling bearing.