Wind Turbine Blade Structural Health Monitoring Using Piezoelectric Sensors and Machine Learning

This paper presents a machine learning framework
for classifying fatigue-induced damage stages in a wind turbine
blade using piezoelectric (PZT) guided-wave measurements. A
cycle-aware, group-based data-splitting strategy is employed to
prevent measurements from the same fatigue cycle from
appearing in both training and test partitions. Four classifiers –
Random Forest, XGBoost, Logistic Regression, and Support
Vector Machine are trained using 44 time- and frequencydomain features extracted from four PZT receiver channels at
six excitation frequencies (20–120 kHz). The dataset comprises
10,968 feature vectors spanning 1,828 unique fatigue-cycle
instances from a Sonkyo Windspot 3.5 kW composite blade
subjected to cyclic loading. Four structural states are
considered: Pre-crack, Crack-3mm, Crack-10mm, and Postoverload. On the held-out test set of 2,196 feature vectors,
Random Forest and XGBoost achieve accuracies of 99.64% and
99.68%, respectively, with AUROC values of 0.9996 for both
models. Logistic Regression and Support Vector Machine
achieve accuracies of 98.00% and 96.68%, respectively.
TreeSHAP analysis identifies dispersion- and energy-related
features from two PZT channels as consistently influential
across the tree-based models. A label-permutation control
reduces accuracy to 34.6% when training labels are shuffled,
supporting the conclusion that the observed classification
performance depends on the original damage-label structure
rather than the partitioning procedure alone. The results
demonstrate the feasibility of machine-learning-based fatiguestage classification on a single benchmark specimen, while crossspecimen generalization remains an open question.