Modulated Gabor filter based deep convolutional network for electrical motor bearing fault classification and diagnosis

Abstract

The high applicability of the electrical motor has led to gain attention in condition monitoring to diagnosis the most common type of fault in this machine, bearing element. The emergence of deep neural networks (DNN) provides the opportunity to design a network for early bearing fault diagnosis with high speed and without any additional feature extraction technique. However, robustness against the noise and some deficiencies in fully capturing features are still challenging issues. To resolve this problem, this paper proposes a one module Gabor filter based convolutional neural networks (CNN), namely Gabor convolutional neural network (GCNN), for bearing fault detection and classification. GCNN is a modulated Gabor filter to enhance the ability in capturing temporal features as well as enhance understanding spatial features with fewer parameters and higher robustness against noises and can be considered as a computational efficient deep structure. The simulation results of the bearing fault detection/classification are studied on two different experimental prototypes, including case Western Reserve University (CWRU) and Paderborn University (Paderborn) datasets. The superiority of this method is shown by comparison with accelerated CNN (ACNN), adaptive CNN, standard CNN, support vector machine (SVM), learning vector quantisation (LVQ), and feedforward neural network (FFNN).