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Stochastic Identification-based Active Sensing Acousto-Ultrasound SHM Using Stationary Time Series Models
In this work, a probabilistic damage detection and identification scheme using stochastic time series models in the context of acousto-ultrasound guided wave-based SHM is proposed, and its performance is assessed experimentally.
Time-varying Identification of Guided Wave Propagation under Varying Temperature via Non-Stationary Time Series Models
In order to circumvent this difficulty, in this paper, a novel stochastic time series-based framework was adopted to model guided wave propagation under varying temperatures.
Gaussian Process Regression for Active Sensing Probabilistic Structural Health Monitoring: Experimental Assessment Across Multiple Damage and Loading Scenarios
The state prediction method presented herein is applied to single-state quantification in the first two test cases, as well as the third one assuming the loading state is known.
Statistical guided-waves-based SHM via stochastic non-parametric time series models
Damage detection in active-sensing, guided-waves-based Structural Health Monitoring (SHM) has evolved through multiple eras of development during the past decades.
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