Next-Generation Condition Monitoring: Digital Twin in RUL Predictive Maintenance for Main Bearings of Semi-Direct Drive

作者：Da Lv, Chao Zhang, Wentao Zhao, Yongzhi Pang · 年份：2025 · DOI：10.1109/iceiom65271.2025.11239920 · 被引用次数：1 · 研究领域：Machine Fault Diagnosis Techniques、Gear and Bearing Dynamics Analysis、Digital Transformation in Industry

Against the backdrop of China's national strategic goals and supporting policies for carbon peak and carbon neutrality, For wind power enterprises, reducing actual operating costs has become a top priority. The main bearing's unexpected damage in Semi-Direct Drive Wind Turbines (SDDWT) due to delayed maintenance can affect the overall machine stability, even causing prolonged shutdowns. Excessive maintenance, however, also leads to a significant increase in operation and maintenance costs. High-precision remaining useful life (RUL) prediction for main bearings can effectively reduce the operation costs of SDDWT, but RUL research on large rotating machinery like SDDWT main bearings still faces critical challenges: unclear performance degradation mechanisms, limited valuable data samples, and severe lack of data labeling.To address the above issues, this paper takes the main bearings of SDDWTs as the research object. By integrating model-based Digital Twin technology with data-driven domain adaptation methods, it conducts studies on: (1) the analysis of performance degradation mechanisms for main bearings; (2) the theoretical methodology of model-data fusion for RUL prediction.