A Graph-Embedded Subdomain Adaptation Approach for Remaining Useful Life Prediction of Industrial IoT Systems

The Industrial Internet of Things (IIoT) greatly facilitates prognostics and health management of complex industrial systems, wherein the vast amount of real-time data from the IIoT improves intelligent predictive maintenance of industrial systems. When processing industrial IoT data across devices, traditional subdomain adaptation-based methods ignore the local similarities across domains. Also, if fault classes are used to define subdomains, these methods may not be applicable when the target domain is unlabeled or has limited labels. To address the above challenges, a Graph-embedded Subdomain Adaptation Network (GSAN)-based approach is proposed to predict the remaining useful life under different machines in IIoT. Specifically, a manifold subdomain representation is established by manifold learning and local manifold discrepancies between each pair of manifold subdomains with the highest similarity are minimized. To maintain a divisible margin for each manifold, a self-supervised intra-manifold regularization module is developed. An extensive evaluation of six transfer scenarios is performed, and the experimental results show that GSAN can achieve more significant outcomes. This can provide some guidance for future work on prognostics across devices and subdomains.