Out-of-domain generalization for remaining useful life prediction of rotating machinery from a single source: An adversarial contrastive learning approach

With the extensive sensor data provided by the Industrial Internet of Things, data-driven remaining useful life (RUL) prediction methods are crucial for enhancing equipment reliability in industrial environments. To enhance prediction accuracy under unknown operating conditions (OCs), domain adaptation and domain generalization-based RUL prediction methods have emerged. However, when only a single-source domain is available, the lack of sample diversity, coupled with significant and unpredictable domain shifts (DSs) between the source and unknown target domains, hinders the model's ability to generalize effectively to the unknown target domain. To address these challenges, a novel RUL prediction method based on adversarial contrastive learning for single-source domain generalization (ACL-SDG) under unknown OCs is proposed. First, a semantic embedding-based multi-pseudo-domain generation (SE-MPDG) module is designed, which generates diverse and valid pseudo-domain samples, guided by the developed subdomain-level supervised contrastive learning loss, subdomain continuity manifold regularization, and semantic consistency constraints to improve the model's out-of-domain generalization capability. Subsequently, a domain-invariant feature-guided RUL prediction (DIF-RP) module is proposed to alleviate DS. This module compels the feature extractor to mine domain-invariant degradation features across different domains, constrained by label-level supervised contrastive learning loss. Finally, adversarial training is conducted between the SEMPDG and DIF-RP modules to further enhance the diversity of pseudo-domains while ensuring the cross-domain invariance of degradation features. Extensive experimental validation of singlesource cross-domain RUL prediction for one practical dataset and two public datasets, under unknown OCs, demonstrates the efficacy and superiority of the proposed method.