A Novel Wind Turbine Fault Diagnosis Method via Deviation-Dynamic Regime Features and Physics-Informed Neural Network

Effective fault diagnosis of wind turbine blades and rotating machinery is critical for ensuring operational reliability and reducing maintenance costs. This study introduces a healthy-reference modeling framework that combines physics-informed neural network (PINN) with deviation-based dynamic regime features for systematic fault detection. At first, healthy and faulty data are normalized, then PINN is trained solely on healthy data, creating a reference model that predicts normal behavior. Deviations between measured signals and the healthy-reference predictions are then analyzed to extract key dynamic regime features, including energy, stability, drift, intermittency, and persistence, capturing subtle variations caused by faults. An interpretable Support Vector Machine (SVM) classifier uses these features to identify fault types such as ball, inner race, outer race, crack, erosion, and unbalance. Classification is performed using dynamic feature combinations while energy is often used as the base feature. The result shows energy with persistence combination performance is better than other feature combinations, and fused features achieved higher accuracy for both datasets. The approach is validated on both bearing data and an experimental blade dataset, demonstrating strong performance across different mechanical systems. Comparative evaluation with three different approaches, including Cross-load Scalogram-based CNN, Spectrogram-based CNN, and Hybrid SVM, highlights that the proposed healthy reference framework offers a data-efficient, interpretable, and robust solution for fault detection. This work highlights the importance of modeling healthy dynamics before classification, capturing both how strong a fault is and how it behaves over time, which offers a practical approach for wind turbine condition monitoring with limited data.