Uncertainty Estimation Pseudo-Labels Guided Source-Free Domain Adaptation for Cross-Domain Remaining Useful Life Prediction in IIoT

Domain adaptation (DA) enhances the scalability of remaining useful life (RUL) prediction technologies, providing a reliable foundation for maintenance decisions across diverse equipment within Industrial Internet of Things (IIoT). Traditional DA approaches typically necessitate simultaneous access to both source and target domain data, which often conflicts with data privacy concerns prevalent in IIoT. Furthermore, the substantial storage resources required for source domain data hinder the implementation of efficient domain adaptation on resource-constrained edge devices. To address these challenges, we propose a source-free domain adaptation (SFDA) framework leverages uncertainty estimation pseudo-labels to conduct cross-domain RUL prediction in the absence of source domain data. Initially, we develop a self-supervised knowledge distillation framework that adapts efficiently to domain shift based on pseudo-labeling. Building on this, we propose an uncertainty estimation pseudo-label guided loss reweighting strategy to mitigate the impact of pseudo-label noise. This strategy prioritizes highly reliable pseudo-labels by assessing discrepancies in feature distributions from different augmented views of the input samples. Additionally, a reconstruction-based training method is designed to align the feature distributions. Extensive experimental evaluations demonstrate that our proposed method outperforms current stateof-the-art techniques, ensuring reliable RUL predictions in scenarios characterized by domain shift and the absence of source domain data.