A supervised vibration-based statistical methodology for damage detection under varying environmental conditions & its laboratory assessment with a scale wind turbine blade

The problem of vibration-based damage detection under varying environmental conditions and uncertainty is considered, and a novel, supervised, PCA-type statistical methodology is postulated. The methodology employs vibration data records from the healthy and damaged states of a structure under various environmental conditions. Unlike standard PCA-type methods in which a feature vector corresponding to the least important eigenvalues is formed in a single step, the postulated methodology uses supervised learning in which damaged-state data records are employed to sequentially form a feature vector by appending a transformed scalar element at a time under the condition that it optimally, among all remaining elements, improves damage detectability. This leads to the formulation of feature vectors with optimized sensitivity to damage, and thus high damage detectability. Within this methodology three particular methods, two non-parametric and one parametric, are formulated. These are validated and comparatively assessed via a laboratory case study focusing on damage detection on a scale wind turbine blade under varying temperature and the potential presence of sprayed water. Damage detection performance is shown to be excellent based on a single vibration response sensor and a limited frequency bandwidth.