Prediction Consistency Guided Convolutional Neural Networks for Cross-Domain Bearing Fault Diagnosis

Abstract

An underlying assumption in bearing fault diagnosis is that the training data from a source domain and the test data from a target domain obey the same distribution. But this assumption can be easily violated in practical industrial environments due to domain shift, which leads to significant performance degradation. To overcome this issue, we propose a novel convolutional neural network model to identify cross-domain bearing fault types based on 1-D vibration signals. Different from current single-network-based approaches, our model comprises a student network and a teacher network that simultaneously conduct data distribution matching and discriminative feature learning. Moreover, the two networks promote each other with the label prediction consistency constraint, so that the discriminative knowledge is able to transfer between the domains. Our model bridges the semantic information of the source vibration signals and the distribution information of the target vibration signals by jointly performing cross-domain feature disentanglement and adaptation. The proposed method is evaluated extensively on the Case Western Reserve University bearing fault dataset in two scenarios: varying working loads and different sensor locations. Experimental results show the superior performance of our method compared with existing shallow and deep learning methods in the literature.