Interpretable operational condition attention-informed domain adaptation network for remaining useful life prediction under variable operational conditions

Remaining useful life (RUL) prediction is critical to formulating appropriate maintenance strategies for machinery health management and is playing a vital role in the field of predictive maintenance. Limited by the time-varying operational conditions, traditional RUL prediction models trained on some run-to-failure (RTF) datasets are unlikely to be generalized to a new degradation process. To enhance the generalizability, recent studies have focused on the development of deep domain adaptation methods for RUL prediction, which mainly align the global temporal features across the source and target domains, resulting in imprecise predictions under time-varying operational conditions. In addition, existing RUL prediction methods are lacking in clear physical significance and interpretability. To address the above-mentioned issues, an operational condition attention (OCA) subnetwork is constructed to eliminate the entanglement between the time-varying operational conditions and monitoring data. Adversarial-based domain adaptation (ABDA) and distance-based domain adaptation (DBDA) methods were utilized respectively to reduce the distribution discrepancy of the temporal features. In this way, two novel domain adaption methods were proposed for RUL prediction with time-varying operational conditions. The comprehensive experiments were conducted on aero-engines to validate the proposed methods. Owing to the explicit modeling of the influence mechanism between the operational conditions and monitoring data, the proposed methods exhibit improved performance as well as higher prediction accuracy than traditional deep domain adaption methods while being certainly interpretable.