Szczegóły publikacji
Opis bibliograficzny
Optimizing sensor cluster placement for acoustic emission source localization in plates using a Bayesian framework / Siddesh RAORANE, Costas Papadimitriou, Paweł PAĆKO // The e-Journal of Nondestructive Testing [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1435-4934 . — 2026 — spec. iss., s. 1–8. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 7–8, Abstr. — EWSHM 2026 : 12th European Workshop on Structural Health Monitoring : July 7–10, 2026, Toulouse, France
Autorzy (3)
- AGHRaorane Siddhesh
- Papadimitriou Costas
- AGHPaćko Paweł
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 169593 |
|---|---|
| Data dodania do BaDAP | 2026-09-23 |
| Tekst źródłowy | URL |
| DOI | 10.58286/33684 |
| Rok publikacji | 2026 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | The e-Journal of Nondestructive Testing |
Abstract
Acoustic emission (AE) techniques are increasingly employed in structural health monitoring (SHM) and non-destructive testing (NDT) to assess the integrity of engineering structures. AE sources are typically associated with deformation and damage mechanisms such as cracking, dislocations, corrosion, inclusions, and delamination. Their localization enables real-time monitoring and supports timely, targeted maintenance to ensure structural safety. AE-based localization relies on a network of spatially distributed sensors that record AE signals. The localization accuracy depends critically on the information captured by these sensors, which in turn is governed by their spatial placement. In practical scenarios, deploying sensors across an entire structure is infeasible, making optimal sensor placement essential. The challenge is to determine a sensor configuration that yields the most informative signals for accurate localization under practical constraints. In this paper, we present a Bayesian optimal sensor placement strategy to determine the optimal locations of sensor clusters that yield the most accurate AE source localization in isotropic plates with unknown properties. Each cluster consists of three sensors arranged in a right-angled triangular configuration. The source location parameters and their uncertainties are estimated using a Bayesian optimal design framework for parameter estimation. Although AE source locations are typically unknown, critical areas where damage is likely to initiate — referred to as hot-spots — are often known. To localize AE sources within these hot-spot regions, at least two sensor clusters are required. The presented Bayesian framework was employed to identify both optimal and worst cluster placements for different hot-spot regions using configurations of 2, 3, and 4 sensor clusters. The Bayesian strategy requires solving an optimization problem, in this study, five different optimization algorithms were employed and compared in terms of the resulting cluster locations and computational efficiency. To validate the approach, experiments were conducted on an aluminum plate. The best and worst cluster locations predicted by the Bayesian design, along with an arbitrary cluster configuration, were positioned on the plate, and AE signals were generated at 10 distinct locations using pencil lead breaks. It was observed that the localization error was smallest for the optimal cluster placements and largest for the worst placements, with the arbitrary configuration yielding intermediate accuracy, as shown in the figure. The proposed Bayesian optimal sensor placement strategy for AE source localization is directly applicable to practical scenarios across mechanical, aerospace, space, and other engineering domains. Since AE-based monitoring of metallic plate-like structures (e.g., aircraft fuselages, bridges, wings) commonly relies on pre-defined hot-spot regions rather than known defect locations, the presented methodology enables sensor networks to be deployed in a manner that maximizes localization accuracy and thereby enhances structural safety.
