Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks

Despite the recent advances on intelligent fault diagnosis of rolling element bearings, existing research works mostly assume training and testing data are drawn from the same distribution. However, due to variation of operating condition, domain shift phenomenon generally exists, which results in significant diagnosis performance deterioration. To address cross-domain problems, latest research works preferably apply domain adaptation techniques on marginal data distributions. However, it is usually assumed that sufficient testing data are available for training, that is not in accordance with most transfer tasks in real industries where only data in machine healthy condition can be collected in advance. This paper proposes a novel cross-domain fault diagnosis method based on deep generative neural networks. By artificially generating fake samples for domain adaptation, the proposed method is able to provide reliable cross-domain diagnosis results when testing data in machine fault conditions are not available for training. The experimental results suggest that the proposed method offers a promising approach for industrial applications.