Cross domain fault diagnosis based on generative adversarial networks

Data-driven fault diagnosis utilizing deep learning algorithms is currently a topic of great interest. Without proper training, data-driven models usually fail to generalize on operating conditions different from the ones used in the training set. The majority of domain adaptation research for machinery fault diagnosis focuses on the transfer between limited working conditions for the same machine. In real-life applications, machines operate under a wide range of operating conditions and the data are mostly available for healthy conditions with seldom failures. Hence, models generated from controlled experiments do not usually generalize well under substantial domain shifts. To address this issue, this paper proposes a semi-unsupervised domain adaptation approach for cross-machine fault diagnosis which integrates model optimization and Generative Adversarial Networks (GANs) to bridge the gap between source and target domains. Experiments of transferring between two bearing data sets show that the proposed method is able to effectively train an optimized model that generalizes on both the source and target domains, and train a generator that learns the source domain probability distribution to substitute for larger domain shifts.

Automated summary

Data-driven fault diagnosis using deep learning algorithms is a popular research topic. However, without proper training, these models often struggle to generalize to different operating conditions. Most research in domain adaptation for machinery fault diagnosis focuses on transferring between similar working conditions. This paper proposes a semi-unsupervised domain adaptation approach that integrates model optimization and Generative Adversarial Networks (GANs) to bridge the gap between different machine domains. Experiments on bearing data sets demonstrate the effectiveness of this method in training a model that generalizes well and a generator that learns the source domain distribution for domain shifts.