Multi-Fault Classification for Gears Based on Discrete Wavelet Transform, Best Features Selection and Improved Support Vector Machine

Intelligent fault detection diagnosis methods are one of the common topics in recently investigations. In this paper, a new hybrid technique is presented based on discrete wavelet transform (DWT) and multi – class support vector machine (Multi-SVM). The considered vibrational signals are collected in three conditions: normal, chipped tooth and worn teeth. These signals are decomposed using DWT methods with different wavelet base functions and the most appropriate level of decomposition are selected by the cross - correlation concept. The feature vector for each sample is extracted using different time domain statistical functions. «One – against - one» support vector machine (SVM-OAO) is utilized for detecting the gearbox conditions. The condition recognition of a gearbox is depended on the extracted features type and setting the SVM parameters. Therefore, in this study, particle swarm optimization (PSO) is used for identifying the most sensitive features to the defect and its type and determining the optimal parameters of SVM method. The obtained results show that the identification accuracy of the gearbox conditions is significantly increased with improving the feature matrix and the SVM classifier method.