A Bayesian methodology for localising acoustic emission sources in complex structures. (15th January 2022)

• Record Type: Journal Article
• Authors: Jones, M.R.; Rogers, T.J.; Worden, K.; Cross, E.J.
• Is Part Of: Mechanical systems and signal processing. Volume 163(2022)
• Publication Date: 2022-01-15
• Subjects: Bayesian -- Acoustic emission -- Localisation -- Gaussian processes -- Complex structure; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108143
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A novel approach to Bayesian acoustic emission localisation is proposed. Gaussian process regression is used to learn difference-in-time-of-arrival information. Mappings are produced that quantify the likelihood of emission location. The maps provide a probabilistic interpretation of the source location prediction. The performance of the method is demonstrated on a complex structure. Abstract: In the field of structural health monitoring (SHM), the acquisition of acoustic emissions to localise damage sources has emerged as a popular approach. Despite recent advances, the task of locating damage within composite materials and structures that contain non-trivial geometrical features, still poses a significant challenge. Within this paper, a Bayesian source localisation strategy that is robust to these complexities is presented. Under this new framework, a Gaussian process is first used to learn the relationship between source locations and the corresponding difference-in-time-of-arrival values for a number of sensor pairings. As an acoustic emission event with an unknown origin is observed, a mapping is then generated that quantifies the likelihood of the emission location across the surface of the structure. The new probabilistic mapping offers multiple benefits, leading to a localisation strategy that is more informative than deterministic predictions or single-point estimates with an associated confidence bound. The performance of the approach is investigated on a structure with numerous complex geometrical features and demonstrates a favourable performance in comparison to other similar localisation methods.