Feature extraction and optimized support vector machine for severity fault diagnosis in ball bearing

Abstract

In this paper, a method for severity fault diagnosis of ball bearings is presented. The method is based on wavelet packet transform (WPT), statistical parameters, principal component analysis (PCA) and support vector machine (SVM). The key to bearing faults diagnosis is features extraction. Hence, the proposed technique consists of preprocessing the bearing fault vibration signal using statistical parameters and energy obtained through the application of Db8-WPT at the third level of decomposition. After feature extraction from vibration signal, PCA is employed for dimensionality reduction. Finally, particle swarm optimization with passive congregation-based support vector machine is used to classify seven kinds of bearing faults. The classification results indicate the effectiveness of the proposed method for severity faults diagnosis in ball bearings.