Szczegóły publikacji
Opis bibliograficzny
Application of artificial neural networks for damage indices classification with the use of Lamb waves for the aerospace structures / Ziemowit DWORAKOWSKI, Łukasz AMBROZIŃSKI, Paweł PAĆKO, Krzysztof DRAGAN, Tadeusz STEPIŃSKI, Tadeusz UHL // Key Engineering Materials ; ISSN 1013-9826. — 2014 — vol. 588, s. 12–21. — Bibliogr. s. 20–21, Abstr. — Dodatkowa afiliacja K. Dragan: ITWL Instytut Techniczny Wojsk Lotniczych. — Smart diagnostics V : selected, peer reviewed papers from the 5th International Congress of Technical Diagnostics, September 3-5, 2012, Krakow, Poland / ed. Tadeusz Uhl. — ISBN 978-3-03785-889-9
Autorzy (6)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 77474 |
|---|---|
| Data dodania do BaDAP | 2013-11-08 |
| Tekst źródłowy | URL |
| DOI | 10.4028/www.scientific.net/KEM.588.12 |
| Rok publikacji | 2014 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Key Engineering Materials |
Abstract
Lamb waves (LW) are used for damage detection and health monitoring due to the long range propagation ability and sensitivity to structural integrity changes as well as their robustness in different applications. However, due to the dispersive character and multimode nature of LWs, analysis of the acquired ultrasonic signals is very complex. It becomes even more difficult when applied to a complex structure, for instance, an aircraft component with riveted joints and stringers characterized by difficult geometries. Therefore, numerous approaches to the evaluation of damage indices have been proposed in the literature. In this paper, comparison a number of damage indices applied to LWs testing in aircraft aluminum panels. The damage indices, known from the literature have been selected from the application point of view. Artificial neural network has been used for the classification of fatigue cracks and artificial damages induced in the specimens taken from a real aircraft structure. Article presents results of simulation, data analysis and data classification obtained using selected and dedicated neural network. The main aim of the presented research was to develop signal processing and signal classification methods for an aircraft health monitoring system. The article presents a part of the research carried out in the project named SYMOST.
