Szczegóły publikacji
Opis bibliograficzny
Voting neural network classifier for detection of fatigue damage in aircrafts / DWORAKOWSKI Ziemiwit, AMBROZIŃSKI Łukasz, DRAGAN Krzysztof, STEPINSKI Tadeusz, UHL Tadeusz // W: EWSHM 2014 : 7th European Workshop on Structural Health Monitoring : July 8–11, 2014 – Nantes, France : conference program & abstract book. — France : Inria - Institut de recherche en Informatique et Automatique, cop. 2014. — S. 112-113. — Pełny tekst W: 7th EWSHM 2014 [Dokument elektroniczny] : European workshop on Structural health monitoring : July 8–11, 2014, La Cité, Nantes. — Wersja do Windows. — Dane tekstowe. — [France : s. n., 2014]. — Dysk Flash. — S. 1894–1901. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 1901, Abstr. — K. Dragan – dod. afiliacja w wersji elektronicznej: ITWL Instytut Techniczny Wojsk Lotniczych
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 82908 |
|---|---|
| Data dodania do BaDAP | 2014-07-28 |
| Rok publikacji | 2014 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | 7th European Workshop on Structural Health Monitoring |
Abstract
An ANN based method for detection and localization of fatigue damage in aircraft structures is presented in the paper. Damage indices are calculated from Lamb-wave measurements conducted by the network of piezoelectric transducers. Data gathered by the sensors is used as an input to the proposed voting neural network classifier. A set of neural network electors of different architecture cooperates to achieve consensus concerning the state of each monitored path. Sensed signal variations in the ROI, detected by the networks at each path, are used to assess the state of the structure as well as to localize detected damage and to filter out ambient changes. The classifier has been extensively tested on large data sets acquired in the tests of specimens with artificially introduced notches as well as the results of numerous fatigue experiments. Effect of the classifier structure and test data used for training on the results is evaluated. It is shown that the developed classifier performs better than individual ANNs in terms of damage detection. The classifier structure, composed of different networks working together, yields an increased reliability, mainly due to the lower impact of the initial weights distribution on the final result. Copyright © Inria (2014).
