A robust Bayesian methodology for damage localization in plate-like structures using ultrasonic guided-waves. (1st May 2019)

• Record Type: Journal Article
• Authors: Cantero-Chinchilla, Sergio; Chiachío, Juan; Chiachío, Manuel; Chronopoulos, Dimitrios; Jones, Arthur
• Is Part Of: Mechanical systems and signal processing. Volume 122(2019)
• Page Start: 192
• Page End: 205
• Publication Date: 2019-05-01
• Subjects: Bayesian inverse problem -- Ultrasonic guided-waves -- Time of flight -- Damage localization -- Multiple damage areas -- Structural health monitoring; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.12.021
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A unified Bayesian approach is proposed for damage localization in thin structures. Using a hyper-robust time-frequency model leads to a more robust damage inference. The method efficiently reconstructs multi-damage areas using ultrasonic guided-waves. Abstract: SHM methods for damage detection and localization in plate-like structures have typically relied on signal post-processing techniques applied to ultrasonic guided-waves. The time of flight is one of these signals features which has been extensively used by the SHM community for damage localization. One approach for obtaining the time of flight is by applying a particular time-frequency transform to capture the frequency and energy content of the wave at each instant of time. To this end, the selection of a suitable methodology for time-frequency transform among the many candidates available in the literature has typically relied on experience, or simply based on considerations about computational efficiency. In this paper, a full probabilistic method based on the Bayesian inverse problem is proposed to rigorously provide a robust estimate of the time of flight for each sensor independently. Then, the robust prediction is introduced as an input to the Bayesian inverse problem of damage localization. The results reveal that the proposed methodology is able to efficiently reconstruct the damage localization within a metallic plate without the need to assume a specific a priori time-frequency transform model.