A Bayesian methodology for localising acoustic emission sources in complex structures

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