A Bayesian methodology for localising acoustic emission sources in complex structures
Matthew R. Jones, Tim J. Rogers, Keith Worden, Elizabeth J. Cross
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.