Active-Sensing Acousto-Ultrasound-based Rotorcraft Structural Health Monitoring via Adaptive Functional Series Models

In this work, the experimental assessment of the damage diagnosis performance of a full-scale rotorcraft blade is performed via stochastic time-varying time series models in the context of active sensing acousto-ultrasound guided wave-based damage detection and identification scheme. Ultrasonic guided waves, that are dispersive in nature, are represented via functional series time-varying autoregressive (FS-TAR) models. Next, the estimated time-varying model parameters are employed within a statistical decision making framework to tackle damage detection and identification under predetermined type I error probability levels. Damage detection and identification based on coefficients of projection (COP) as well as time-varying model parameters are shown. Both damage intersecting and non-intersecting paths are considered in a full-scale rotorcraft blade as well as in an aluminum plate in pitch-catch configuration for the complete experimental assessment. The detailed damage diagnosis results are presented and the method's robustness, effectiveness, and limitations are discussed.