Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme

Wu, Wen; Cantero-Chinchilla, Sergio; Yan, Wang Ji; Chiachio Ruano, Manuel; Remenyte-Prescott, Rasa; Chronopoulos, Dimitrios

Abstract

In this paper, defect detection and identification in aluminium joints is investigated based on guided wave monitoring. Guided wave testing is first performed on the selected damage feature from experiments, namely, the scattering coefficient, to prove the feasibility of damage identification. A Bayesian framework based on the selected damage feature for damage identification of three-dimensional joints of arbitrary shape and finite size is then presented. This framework accounts for both modelling and experimental uncertainties. A hybrid wave and finite element approach (WFE) is adopted to predict the scattering coefficients numerically corresponding to different size defects in joints. Moreover, the proposed approach leverages a kriging surrogate model in combination with WFE to formulate a prediction equation that links scattering coefficients to defect size. This equation replaces WFE as the forward model in probabilistic inference, resulting in a significant enhancement in computational efficiency. Finally, numerical and experimental case studies are used to validate the damage identification scheme. An investigation into how the location of sensors can impact the identified results is provided as well.

• Journal Article Type: Article
• Acceptance Date: Apr 21, 2023
• Online Publication Date: Apr 21, 2023
• Publication Date: 2023
• Deposit Date: Jun 22, 2023
• Publicly Available Date: Jun 22, 2023
• Journal: Sensors
• Electronic ISSN: 1424-8220
• Publisher: MDPI AG
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 8
• Article Number: 4160
• DOI: https://doi.org/10.3390/s23084160
• Keywords: guided waves; joints/bounded structures; damage identification; Bayesian inference; hybrid wave and finite element; surrogate model
• Public URL: https://nottingham-repository.worktribe.com/output/20008714
• Publisher URL: https://www.mdpi.com/1424-8220/23/8/4160