Remaining Useful Life Prediction via Information Enhanced Domain Adversarial Generalization

Predicting remaining useful life (RUL) plays a crucial role in predictive maintenance, improving system reliability, availability, and safety. However, obtaining data from the target domain is often challenging in real-world industrial applications. This article focuses on the domain generalization (DG) problem, where the attention is directed toward adapting algorithms to unseen domains. Building upon the popular algorithm domain adversarial neural network (DANN) for DG, we extend the contrastive adversarial domain adaptation method using a multiple source-source adversarial network to learn domain-invariant features from multiple source domains. In addition, we incorporate the swin-transformer structure into our model to enhance its capability in extracting time-frequency features, leveraging its excellent performance in visual DG problems. Furthermore, to expand the training dataset, we propose a novel augmentation algorithm for time-frequency data. Through predictive experiments in scenarios with unknown domain labels, we validate the contribution of the proposed methods to RUL prediction performance.