Bearing Fault Automatic Classification Based on Deep Learning

Abstract

An automatic classification method based on deep learning for bearing fault diagnosis is proposed. The method is designed with the ability of faulty signal automatic clustering without human knowledge. A dataset in which each sample is given a random label is configured after extracting the features of vibration signals from the frequency domain. The dataset is used to train a deep neural network (DNN) to obtain the initial classification. The classification results are assessed by testing the subsignals extracted from the raw data, and the sample labels are modified according to the evaluation result. The modified dataset is used to train the DNN a second time. Samples with characteristic faults are clustered in various classes after iterating the DNN training and testing. The proposed method is tested with the bearing data provided by the Case Western Reserve University (CWRU) Bearing Data Center, which is a standard reference to test fault detection algorithms. The 12k drive end, 48k drive end, and 12k fan end CWRU bearing data are classified into 7, 6, and 4 groups, respectively. The testing results show that the proposed method can achieve automatic clustering for vibration signals with a variety of faults.