Mitigating Negative Transfer Learning in Source Free-Unsupervised Domain Adaptation for Rotating Machinery Fault Diagnosis

Driven by the data-centric industrial revolution, prognostics and health management (PHM) technology based on sophisticated deep learning (DL) methods has become crucial for industrial fault diagnosis. Based on well-labeled data, the DL methods further improve PHM development. However, the efficiency of the involved DL methods is impacted significantly due to the limited data collection. To address this problem, source-free unsupervised domain adaptation (SF-UDA) has been proposed to improve DL efficiency without fully labeled source data. However, the traditional SF-UDA methods are constrained by predefined label numbers and insufficient distribution alignment. To solve this problem, we propose the use of initial pseudo labels (IPLs) generated through adaptive bandwidth-based mean shift clustering (ABMSC), which are then refined using cosine similarity with unlabeled target data, bolstering SF-UDA's efficiency. Furthermore, we introduce weight-aware regularization (W-AR) to effectively mitigate negative transfer. Experimental results show significant accuracy improvements, with gains of 1.5%-6.7% and 1.3%-5% in the same operating environment, and 0.4%-24.8% and 3.1%-8.6% in different operating environments.