Incremental Contrastive Learning With Dual Distilling for Source-Free Domain Adaptation in Industrial Process Fault Diagnosis

Source-free domain adaptation (SFDA) enables knowledge transfer without source data, addressing privacy constraints of the transfer learning. However, existing methods often depend on fixed confidence thresholds for pseudolabeling, which are poorly adaptable and lead to unstable performance. Moreover, class distribution mismatch is frequently ignored, further hindering adaptation. To tackle these challenges, incremental contrastive learning with dual distilling for SFDA is proposed and applied in industrial process fault diagnosis in this article. A threshold-free pseudolabeling strategy is first introduced to dynamically assess label reliability. Then, a stage-wise incremental contrastive learning framework progressively expands from per-class Top-K samples to the full target set, effectively mitigating class imbalance. In addition, a dual distilling mechanism at both feature and label levels is employed to alleviate model drift caused by forgetting source knowledge. Finally, extensive experiments on three-phase flow and wastewater treatment datasets demonstrate the effectiveness of the proposed method.