A Bayesian methodology for localising acoustic emission sources in complex structures

TL;DR

This paper introduces a Bayesian approach using Gaussian processes for localising acoustic emission sources in complex structures, improving accuracy and confidence over traditional methods.

Contribution

It presents a novel probabilistic localisation framework that handles complex geometries in structural health monitoring using Gaussian processes.

Findings

Favourable localisation performance on complex geometrical structures

Probabilistic mapping provides more informative results than deterministic methods

Robustness to structural complexities demonstrated in experiments

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.