Classification of Wind Turbine Blade Damage Severity Under Variable Rotational Speed Using Computed Order Tracking and Machine Learning

Wind turbine blades endure cyclic loading and variable aerodynamic stresses that make them prone to cracks, requiring early detection for predictive maintenance. This work classifies blade condition into four categories (healthy and with light, intermediate, and severe damage) from triaxial accelerometer signals under rotor speed varying between 0 and 12 rps. Robustness to variable speed is achieved through computed order tracking, using a tachometer signal to re-express vibration signals in the angle domain. From overlapping angle-domain windows, 130 non-redundant time- and frequency-domain descriptors are computed across three accelerometer axes (43 per axis) plus mean rotor speed. A fully nested cross-validation procedure, repeated within each leave-one-trial-out fold to avoid bias, first determines the discriminative axis, then sizes the final feature set to each classifier’s statistical criterion. The search converges on a single axis (Vy), outperforming an unrestricted three-axis search. After redundancy pruning, the random forest retains eight features and the support vector machine thirteen, the former a subset of the latter, reaching 94.9% and 95.2% accuracy; severe damage is recognized with up to 98.4% recall. Regarding the features, mean rotor speed is retained by both classifiers, indicating that order tracking mitigates, but does not completely eliminate, speed dependence. Moreover, severely damaged blades operate at lower speed than other conditions (Kruskal-Wallis, p<0.0001), suggesting that rotor speed itself provides a genuine damage signature.