Probabilistic Active Sensing Acousto-Ultrasound SHM Based on Non-Parametric Stochastic Representations

Abstract

Existing Structural Health Monitoring (SHM) techniques generally depend on deterministic parameters in order to detect, localize, and quantify damage. This limits the applicability of such systems in real-life situations, where stochastic, time-varying structural response, as well as complex damage types immersed in operational/environmental uncertainties are almost always encountered. Thus, there lies a need for the proposal of statistical quantities and methods for assessing structural health. That is, a holistic probabilistic SHM framework involving damage detection, localization, and quantification, is due if such systems are to become standard on VTOL platforms. In this work, a novel probabilistic approach for active-sensing acousto-ultrasound SHM targeting damage detection and quantification is proposed based on stochastic non-parametric time series representations. Statistical signal processing techniques are used to formulate statistical hypothesis tests, based on which a decision can be made to whether a component is healthy or damaged within pre-defined confidence bounds. The methods presented herein can also be used for damage quantification. The proposed framework is first applied to a notched Aluminum coupon with different damage sizes within an active-sensing, local "hot-spot" monitoring framework. After that, experimental data collected over a stiffened Aluminum panel, representing a sub-scale fuselage component, is analyzed using the probabilistic framework for validation of the proposed methods on more real-life structures. Results show the advantage of the proposed techniques in citing confidence to the decision-making process when compared with state-of-the-art damage indicators. In addition, insights into damage localization within a probabilistic framework are also presented, which may be used as a preliminary step to damage localization algorithms, decreasing the computational cost, and increasing the accuracy of imaging techniques under uncertainty.