Support Vector Machine for mechanical faults classification

Abstract

Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents an SVM based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-fault SVM classifier based on binary classifier is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the multi-fault SVM classifier has good classification ability and high efficiency in mechanical system. It is suitable for on line diagnosis for mechanical system.