Towards Unified Probabilistic Rotorcraft Damage Detection and Quantification via Non-parametric Time Series and Gaussian Process Models

The complex dynamics of rotorcraft structures under varying operational and environmental conditions demand the
development of accurate and robust-to-uncertainties structural health monitoring (SHM) approaches. The inherent
uncertainty within monitoring data makes it difficult for conventional methods to accurately and robustly detect and
quantify damage without the need for a large number of data sets. In addition, due to the time-varying nature of rotorcraft
operations, such conventional metrics might still fail even with abundance of data. In this paper, we propose
a unified probabilistic damage detection and quantification framework for active-sensing, guided-wave SHM that focuses
on monitoring rotorcraft structural “hotspots”. The proposed framework involves three stages: The first stage
incorporates statistical damage detection based on stochastic non-parametric time series (NP-TS) models of ultrasonic
wave propagation signals within a hotspot sensor network configuration. The second stage involves the statistical path
selection, where a NP-TS representation is used for the sole purpose of identifying damage-intersecting signal (wave
propagation) paths, that is the paths that are most sensitive to damage, in order to use them in the subsequent damage
quantification stage. That last stage achieves probabilistic damage quantification, where the results of the NP-TS models
are used to train Bayesian Gaussian Process regression and classification models. This unified framework ensures
accurate and robust damage detection and quantification in a data-efficient manner since only damage-intersecting
paths are selected and used in the analysis. The performance of the proposed framework is compared to that of conventional
state-of-the-art damage indices (DIs) in detecting and quantifying simulated damage in two representative
coupons: a Carbon Fiber Reinforced Polymer (CFRP) coupon and a stiffened aluminum (Al) panel. It is shown that
the proposed framework outperforms conventional DI-based active-sensing guided-wave SHM methods.