Enhancing Learning in Fine-Tuned Transfer Learning for Rotating Machinery via Negative Transfer Mitigation

Abstract

Rotating machinery plays a crucial role in industries such as petroleum, automotive, and food processing, relying on bearings for smooth movement. However, the wear and degradation of bearings due to fluctuating speeds, excessive loads, and prolonged operation pose significant challenges. Therefore, highlighting the essential role of real-time monitoring and diagnostics is critical for preventing machinery failures, improving equipment reliability, and reducing maintenance costs. While Predictive Maintenance (PdM) is widely employed for machinery health monitoring using sensor data, the analysis of diverse and voluminous data poses notable difficulties. To address this problem, we first propose a Normalized Exponential Power Entropy (NEPE)-based feature extraction method, which facilitates fault diagnosis when combined with a Neural Network (NN). Integrating Deep Learning (DL) into Prognostics and Health Management (PHM) improves fault diagnosis but faces limited labeled data and generalizability issues. Although Transfer Learning (TL) overcomes these limitations, fine-tuning with limited target domain data can lead to Negative Transfer Learning (NTL) and Catastrophic Forgetting (CF). To address this problem, we introduce Dynamic-Feature Adaptive Thresholded Normalization (D-FATN), a novel regularization approach that selectively modulates normalized input features during fine-tuning. D-FATN prevents NTL and mitigates CF, improving fault diagnosis. Experimental results demonstrate that our proposed method can improve 7.35% to 30.55% accuracy compared with other feature extraction methods. Additionally, our proposed D-FATN further improves 1.4% to 11.7% accuracy under identical and varying working conditions across both public and real measurement datasets.