Rolling Bearings Fault Diagnosis under Variable Conditions Using RCMFE and Improved Support Vector Machine

As critical components, rolling bearings are widely used in a variety of rotating machinery. It is necessary to develop a suitable fault diagnosis method to prevent malfunctions and breakages of bearings during operation. However, the current methods for the fault diagnosis of rolling bearings are too cumbersome to be applied in practical engineering. In addition, the working condition of rolling bearings is generally tough, complex, and especially variable. These conditions cause fault diagnosis methods to be less effective. This paper aims to provide a simple and effective method for the fault diagnosis of rolling bearings under variable conditions. The main contribution of this paper is as follows: (1) The refined composite multiscale fuzzy entropy (RCMFE) is applied in bearing fault feature extraction because of its simplicity and high efficiency; (2) The improved support vector machine (ISVM), based on the whale optimization algorithm (WOA), is proposed to identify the fault pattern of rolling bearings. The ISVM is proposed in this paper to solve the problem that parameter setting affects the classification effect of SVM. In the ISVM, the WOA is employed to optimize both the regularization and kernel parameters of the SVM. Compared with the traditional optimization methods, the WOA has the advantages of high optimization speed and better optimization ability; (3) Combining the RCMFE and the ISVM to diagnose bearing fault under variable working conditions. The effectiveness of the RCMFE-ISVM has been validated via experimental vibration signal of bearings faults under variable working conditions.