# A Bayesian methodology for localising acoustic emission sources in complex structures

arXiv: https://arxiv.org/abs/2012.11058 · HTML: https://mlsanity.com/paper/2012.11058/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [A spectrum of physics-informed Gaussian processes for regression in engineering](https://mlsanity.com/paper/2309.10656/) (2023)
- [Physics-informed machine learning for Structural Health Monitoring](https://mlsanity.com/paper/2206.15303/) (2022)
- [Constraining Gaussian processes for physics-informed acoustic emission mapping](https://mlsanity.com/paper/2206.01495/) (2022)
- [Meta-models for transfer learning in source localisation](https://mlsanity.com/paper/2305.08657/) (2023)
- [A Bayesian Approach for Shaft Centre Localisation in Journal Bearings](https://mlsanity.com/paper/2203.11719/) (2022)
- [Physics-guided impact localisation and force estimation in composite plates with uncertainty quantification](https://mlsanity.com/paper/2507.13376/) (2025)

## Abstract (arXiv)

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.