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Modern-day civil, mechanical, and aeronautical structures are transitioningtowards a continuous, online, and automated maintenance paradigm in order toensure increased safety and reliability. The field of structural healthmonitoring (SHM) is playing a key role in this respect and active sensingacousto-ultrasound guided-wave based SHM techniques have shown great promisedue to their potential sensitivity to small changes in the structure. However,the methods' robustness and diagnosis capability become limited in the presenceof environmental and operational variability such as changing temperature. Inorder to circumvent this difficulty, in this paper, a novel stochastic timeseries-based framework was adopted to model guided wave propagation undervarying temperatures. Different stochastic time-varying time series models,such as Recursive Maximum Likelihood Time-varying Auto-Regressive (RML-TAR) andRecursive Maximum Likelihood Time-varying Auto-Regressive with ExogenousExcitation (RML-TARX) models are put forward to model and capture theunderlying dynamics of guided wave propagation under varying temperatures. Thesteps and facets of the identification procedure are presented and clearlyexplained. Then the identified models are used to perform one-step-aheadprediction as well as "simulation" of the guided wave signals. In order to gaininsight from a physics perspective, high-fidelity finite element (FE) modelswere also established to model the effect of temperature variation on guidedwave propagation. Finally, surrogate models are formulated through the use ofstochastic time-dependent RML-TARX models and compared with the FE models undervarying temperatures.

Time-varying Identification of Guided Wave Propagation under Varying Temperature via Non-Stationary Time Series Models