SCADA-driven lightweight wide-spectrum bi-temporal fusion network for structural health monitoring of wind turbine blade aerodynamic imbalance

Abstract

Early diagnosis of wind turbine blade aerodynamic imbalance remains challenging because early fault features are weak and easily obscured by operating-condition fluctuations and environmental noise. To improve structural health monitoring reliability, this study proposes a lightweight wide-spectrum bi-temporal fusion network (L-WS-BFN). Based on generator-speed data from an 8.35 MW wind turbine, a preprocessing pipeline with rated-condition constraints, sample-wise standardization, and online probabilistic augmentation was developed. This pipeline reduces the effects of start-up/shutdown transients, control switching, and outlier noise on model learning. Guided by the rotor aerodynamic-load—drivetrain torsional vibration mechanism, the proposed framework integrates wide-spectrum convolution, bi-temporal fusion, and lightweight decision-making for non-stationary 1P modulation. The Bi-Temporal Fusion Module (BFM) improves amplitude—phase representation of slowly varying 1P disturbances through adjacent-segment comparison and competitive attention fusion. L-WS-BFN achieved 95.4% training accuracy with only 0.01855 M parameters and good class balance in precision, recall, and F1-score. Comparative experiments, ablation studies, network-depth sensitivity analysis, and t-distributed stochastic neighbor embedding (t-SNE) visualization confirmed its noise robustness, generalization ability, and edge-deployment suitability. Two normal-state records from August 2025, collected from the target turbine and another turbine, were further used for external normal-only validation. The results support its seasonal specificity and cross-turbine false-alarm robustness, indicating an efficient and reliable edge-side diagnostic solution for wind turbines.