Title: Assessing structural health of helicopter fuselage panels through artificial neural networks hierarchies

Authors: Antonio Candelieri; Raul Sormani; Gaia Arosio; Ilaria Giordani; Francesco Archetti

Addresses: Department of Computer Science, Systems and Communication, University of Milano Bicocca, viale Sarca 336, Building U14, Milan 20126, Italy ' Department of Computer Science, Systems and Communication, University of Milano Bicocca, viale Sarca 336, Building U14, 20126 Milan, Italy ' Consorzio Milano Ricerche, via Cozzi 53, 20126 Milan, Italy ' Consorzio Milano Ricerche, via Cozzi 53, 20126 Milan, Italy ' Department of Computer Science, Systems and Communication, University of Milano Bicocca, Viale Sarca 336, Building U14, 20126 Milan, Italy; Consorzio Milano Ricerche, via Cozzi 53, 20126 Milan, Italy

Abstract: Online assessment of the structural health of aircrafts is crucial both in military and civilian settings. In this paper, Artificial Neural Networks (ANNs) are exploited to obtain a reliable system performing two tasks: diagnosis and prognosis. Diagnosis is devoted to (a) detect a crack, (b) identify the component of the panel involved (bay or stringer) and (c) estimate crack centre and size. Prognosis aims at estimating the evolution of the crack and the Remaining Useful Life (RUL). Training of the ANNs is performed on data sets built through finite elements simulation. Two different ANN hierarchies are presented for diagnosis. Crack evolution is performed for cracks on bay and stringer, separately. Two ANNs are used to estimate the parameters of a crack propagation model (NASGRO equation) for RUL prediction.

Keywords: structural health monitoring; artificial neural networks; hierarchies of classifiers; helicopter fuselage; fuselage panels; aircraft structures; helicopters; reliability; fault diagnosis; crack detection; finite element analysis; FEA; simulation; bays; stringers; aluminium panels; crack propagation models; modelling; crack localisation.

DOI: 10.1504/IJRS.2013.057091

International Journal of Reliability and Safety, 2013 Vol.7 No.3, pp.216 - 234

Available online: 10 Oct 2013 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article