Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately is very important to ensure the reliable operation of rotating machinery. In this paper, a multi-fault classification model based on the kernel method of support vector machines (SVM) and wavelet frame, wavelet basis were introduced to construct the kernel function of SVM, and wavelet support vector machine (WSVM) is presented. To seek the optimal parameters of WSVM, particle swarm optimization (PSO) is applied to optimize unknown parameters of WSVM. In this work, the vibration signals measured from rolling element bearings are preprocessed using empirical model decomposition (EMD). Moreover, a distance evaluation technique is performed to remove the redundant and irrelevant information and select the salient features for the classification process. Hence, a relatively new hybrid intelligent fault detection and classification method based on EMD, distance evaluation technique and WSVM with PSO is proposed. This method is validated on a rolling element bearing test bench and then applied to the bearing fault diagnosis for electric locomotives. Compared with the commonly used SVM, the WSVM can achieve a greater accuracy. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on the vibration signals.

► A multi-fault classification model is proposed based on PSO algorithm and WSVM.
► The PSO algorithm is employed to select the parameters of WSVM.
► WSVM classifiers based on different wavelet kernels are presented.
► Classification accuracies are improved using wavelet kernels compared to the normal RBF kernel.