Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions

Since classical deep learning (DL) techniques are hungry for massive data and suffer from domain shift, domain adaptation (DA) methods are broadly adopted in prognostics and health management (PHM) to align source and target domains. However, DA relies on target datasets collected in advance, which are not always available in practice. In this paper, a domain generalization (DG) approach, which learns from multiple source domains and generalizes well to unseen domains, is introduced for remaining useful life (RUL) prediction of bearings under unseen operating conditions. Specifically, we propose an adversarial out-domain augmentation (AOA) framework to generate pseudo-domains, thereby increasing the diversity of available samples. Hence, a generator is trained in an adversarial manner to generate augmented pseudo-domains by maximizing the domain discrepancy of the latent representations. In addition, we add manifold and semantic regularization to its objective function to ensure the consistency of the pseudo-domains. Trained with these available domains, a task predictor can improve the generalization in inaccessible target domain. Based on this, we provide a specific implementation of AOA-based RUL prediction for DG and validate its effectiveness and superiority using experimental datasets.