Bearing fault diagnosis employing gabor and augmented architecture of convolutional neural network

Abstract

The vast impact on machinery that is rooted by bearing degradation thus pinpointing bearing fault diagnosis as indubitably very crucial. The research is innovated to diagnose the fault in bearing by implementing deep learning approach which is Convolutional Neural Network (CNN) that has superiority over image processing and pattern recognition. A novel model comprises of Gabor transform and augmented CNN is proposed whereby Gabor transform is utilized in representing the raw vibration signals into its 2D image representation, Gabor spectrogram. The augmented CNN is formed by alteration of the present CNN architecture. Gabor spectrogram are fed into the augmented CNN for training and testing in diagnosing the faults of bearings. To date, the method combination for bearing fault diagnosis application is inadequate. Plus, the usage of Gabor transform in mechanical area especially in bearing fault diagnosis is meagrely reported. At the end of the research, it is perceived that the proposed model comprises of Gabor transform and augmented CNN can diagnose the bearing faults with eminent accuracy and perform better than when CNN is fed with raw signals.