Hybrid loss-driven source-free domain adaptation with post-hoc explainability for bearing fault diagnosis

Abstract

While conventional fault diagnosis excels in steady-state scenarios, reliable detection under variable operating conditions remains a critical challenge in modern industrial drive systems. Although domain adaptation techniques address cross-domain distribution shifts, their deployment is often hindered by data privacy concerns and the impracticality of labeling massive target data in real-world factories. Furthermore, standard methods typically require concurrent access to source and target data, which violates the data isolation protocols common in industrial scenarios. To overcome these barriers, this paper proposes a novel and robust source-free domain adaptation (SFDA) framework equipped with post-hoc explainability for bearing fault diagnosis. First, to combat complex environmental interference and uneven sample distributions, we introduce a Gaussian noise-based stochastic perturbation strategy, forcing the model to learn robust decision boundaries. Second, high-confidence pseudo-labels are mined as reliable supervision signals. To fundamentally optimize the feature manifold, we introduce a contrastive learning-based representation strategy. Specifically, a mixed loss combining Supervised Contrastive loss and dot product loss is proposed to explicitly enhance feature discriminability while preventing feature collapse, achieving a delicate balance between intra-class compactness and overall diversity. Finally, a post-hoc visualization mechanism is incorporated to explain the model’s decision-making process, thereby enhancing the transparency and credibility of the diagnosis for practical industrial deployment. Extensive experiments on two publicly available bearing fault datasets demonstrate that the proposed method significantly outperforms existing SFDA-based baselines. On the PU dataset, our approach achieves an average diagnostic accuracy of 99.11%, yielding a performance gain of 1.86% over the state-of-the-art SDALR method. Similarly, on the JNU dataset, the proposed framework attains an average accuracy of 98.36%, demonstrating superior robustness and trustworthiness in real-world fault diagnosis.