A simulation model based fault diagnosis method for bearings

Abstract

Bearings are essential parts in mechanical transmission systems, and their running states directly affect the reliability and stability of the systems. Therefore, an efficient diagnosis method is necessary to detect faults in bearings. In the present, a simulation model based fault diagnosis method for bears is proposed by combination of finite element method (FEM), wavelet packet transform (WPT) and support vector machine (SVM). In this method, firstly, the agreeable finite element models to simulate faulty bearings are presented to obtain the vibration response signals. Secondly, the vibration signals are decomposed into eight signal components using WPT. Ten time-domain feature parameters of all the signal components are calculated to generate the training samples to train the SVM. Finally, the eight signal components decomposed by WPT from the measured vibration signal in a bear, which are serve as a test sample into the trained SVM, and the work condition of the bearing can be determined. Experimental investigations are performed to verify the effectiveness of the present method. The classification accuracy rates for four type faults, i.e., inner race fault, rolling body fault, outer race fault and the combination of rolling body and outer race faults, are 79%, 81%, 71% and 76%, respectively.