A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network

Abstract

Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN is pro-posed. First, the measured vibration signals are transformed into a new data form called multi-ple-domain image-representation. By this transformation, the task of signal-based fault diagno-sis is transferred into the task of image classification. After that, a DNN with a multi-branch structure is proposed to handle the multiple-domain image representation data. The multi-branch structure of the proposed DNN helps to extract features in multiple domains simultane-ously, and to lead to better feature extraction. Better feature extraction leads to a better perfor-mance of fault diagnosis. The effectiveness of the proposed method is verified via the experi-ments conducted with actual bearing fault signals and its comparisons with well-established published methods.