Intelligent method for faults diagnosis of rolling bearings via chaos optimized support vector machine

Abstract

In a transmission system, the faults of rolling bearings occur very frequently. A tiny crack may cause huge damage on the system. Therefore, it is essential to detect the faults of rolling bearings. However, the single fault has been researched extensively while very few works have been done on the multiply faults detection (i.e., simultaneous existence of 2 or more fault types). To deal with this problem, a new method is proposed to diagnosis multi-fault of rolling bearings in this study. The vibration data was analyzed in the time and frequency domains. Then the Support Vector Machine (SVM) was used to recognize the fault patterns. In order to enhance the generalization ability of the SVM diagnosis model, the Chaos algorithm was adopted to optimize the structural parameters of the SVM. Experimental tests have been carried out on a fault simulation setup. The fault detection results show that the proposed method is competent for the multi-fault diagnosis of rolling bearings. The fault detection rate is beyond 90.0%. © Maxwell Scientific Organization, 2013.