Domain adaptation-based deep feature learning method with a mixture of distance measures for bearing fault diagnosis

Abstract

The success of deep learning-based bearing fault diagnosis depends on (a) training data and test data following the same data distribution; (b) a mass of labeled fault data being available. However, the working conditions of the bearings is changing, which leads to a difference in data distribution, and it is very laborious to obtain a mass of labeled data with fault information. To address these issues, a domain adaptation-based deep feature learning method with a mixture of distance measures for bearing fault diagnosis is proposed. First, the noise in vibration signal is filtered by wavelet packet decomposition and reconstruction. Then, frequency slice wavelet transform is used to transform the reconstructed signal into a two-dimensional time–frequency image. Furthermore, a domain-adaptative deep neural network based on ResNet50 is used for bearing fault diagnosis under different working conditions. A mixture of distance measures is used to minimize the distribution discrepancy between source and target domains. The bearing datasets provided by Case Western Reserve University and Paderborn University verify the effectiveness of the proposed method.