Transfer Learning with Unsupervised Domain Adaptation Method for Bearing Fault Diagnosis

Although bearing fault diagnosis methods based on deep learning are very popular in recent years and a lot of brilliant results have been achieved, they assume that the distribution of training samples is same with test samples. However, the working condition of bearing is variable, and labeling fault tags for all data is time-consuming and laborious. In order to solve the problem of lacking labeled data in cross domain scenario, a novel domain adaptation transfer learning based fault diagnosis method based on deep domain adversarial network is proposed. In this method, a deep convolutional neural network (CNN) is used to extract features from raw vibration signals. Then a discriminator and a classifier are applied to minimize the distribution difference of cross-domain features. Experiments are carried out on three benchmark datasets, and the results show that the accuracy of proposed methods is higher than other existing unsupervised transfer learning methods.