Knowledge-driven subdomain adaptation for bearing fault diagnosis

Bearing fault diagnosis is crucial for ensuring the reliability and safety of rotating machinery in industrial settings. Machine learning-based diagnostic models offer powerful solutions, but their effectiveness is challenged by substantial domain shifts caused by variations in operating conditions, such as changes in motor load. To address this challenge, a novel domain adaptation framework that combines physical domain knowledge with deep learning techniques is proposed. The framework employs envelope spectrum analysis to generate reliable pseudo-labels for unlabeled target domains. By incorporating local maximum mean discrepancy into the training process, the framework aligns feature distributions between source and target domains while preserving class-specific information. This method enhances the adaptability of diagnostic models to real-world industrial conditions, reducing the need for extensive labeled data and improving predictive reliability across different operating scenarios. Experimental results performed on the Case Western Reserve University bearing dataset demonstrate that our method outperforms baseline models, achieving superior classification accuracy under significant domain shifts. By improving fault detection under varying load conditions, this approach contributes to more efficient predictive maintenance, reducing unexpected failures and operational downtime in industrial machinery. This approach highlights the potential of combining physics-based insights with deep learning to enhance fault diagnosis in diverse and complex industrial scenarios.