Fatigue Damage Identification and Life Prediction Methods for Offshore Wind Power Support Structures Under Extreme Meteorological Conditions

This paper addresses the challenges of difficult fatigue damage identification and insufficient accuracy in life prediction for offshore wind turbine support structures under extreme environmental conditions. It proposes a damage identification method based on variational mode decomposition (VMD) and fuzzy C-means (FCM) clustering, combined with life prediction using the improved Miner's rule and a Weibull probability model. Damage features are extracted through hot-spot stress analysis and a multi-physics coupling model, while VMD and FCM are employed to enhance identification performance. Stress ratio modification and probability distributions are introduced to quantitatively describe the nonlinearity and uncertainty in damage accumulation. Validation using real-world engineering data demonstrates that the proposed method effectively improves the accuracy of damage identification, reduces the false alarm rate, and yields more reliable life prediction results, indicating its strong value for engineering applications.