Research on Fault Diagnosis Method of Rolling Bearing Based on Feature Optimization and Self-Adaptive SVM

Abstract

Aiming at the problems of the difficulty of extracting the fault sensitive features in the mixed domain of bearings under complex working conditions and the lack of self-adaptability of support vector machine (SVM) parameters, to solve these problems, we investigate a diagnosis method combining optimal feature selection and self-adaptive SVM. Firstly, the optimal feature space through multicluster feature selection (MCFS) is constructed. Then, the differential evolution (DE) strategy is used to improve the search performance of gray wolf optimizer (GWO) to improve the diagnosis accuracy of SVM and make it strong self-adaptability. The experimental results show that, with a small number of sensitive features containing significant category differences, the proposed method not only guarantees the construction of the optimal feature space under the minimum feature dimension, but also greatly improves the accuracy of fault recognition. Simultaneously, compared with the traditional feature reduction method (PCA), the proposed MCFS combined diagnosis model improves the accuracy from 98.5% to 100% with 1.5%. Concurrently, compared with MPA-SVM, GWO-SVM, and PSO-SVM, the convergence performance is improved by 61.54%, 78.26%, and 92.64%, respectively. It can seek the best classification performance in the shortest time. The effectiveness and superiority of the proposed method are fully verified.