Advanced stochastic FEM-based artificial neural network for crack damage detection
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hdl:2117/327586
Tipus de documentText en actes de congrés
Data publicació2011
EditorCIMNE
Condicions d'accésAccés obert
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Abstract
Structural Health Monitoring (SHM) is nowadays one of the most challenging
research fields. As a matter of fact, if from one hand the aerospace industry is trying to extend
the duration of life-limited components, from the other hand a deep control is necessary over
the structures to guarantee both the machine availability and reliability. In effect, thanks to the
advance in the evaluation of the actual structural health by means of a SHM system, it could
be possible to set a Condition Based Maintenance (CBM). This approach means substituting a
component according to its real structural conditions instead of relying just on the design
assumptions. The final aim is to update the scheduled maintenance intervals according to the
actual condition of the structures. However this is not an easy task, as it is governed and
influenced by many variables, each one characterized by a stochastic distribution. In
particular, the key factor is the disposal of detection and monitoring systems as reliable as
possible in order to conjugate safety with economics objective. On the basis of this all the
machine stops can be optimized in order to exploit the machine availability with the minimum
loss of reliability. Thus, the first step for developing such advanced technology would be the
disposal of a robust damage detection system, able to recognise, locate and quantify the
damage in a certain component. The aim of the present work is to define a methodology that
combines the use of Finite Element Models (FEM) with Artificial Neural Networks (ANN)
[1] for crack detection over a typical aerospace structure consisting of a riveted aluminium
skin stiffened with some reinforcing elements [2]. Numerical models, in fact, could be used to
train ANN. A basic system knowledge would result, upon which to introduce the variability
by means of real sensor network data [3], in order to consider the problem from a statistical
point of view. Finally, a proposal for the sensor network characterization in terms of
Probability of Detection (PoD) and False Alarm (PFA) is also reported.
CitacióSbarufatti, C.; Manes, A.; Giglio, M. Advanced stochastic FEM-based artificial neural network for crack damage detection. A: COUPLED IV. "COUPLED IV : proceedings of the IV International Conference on Computational Methods for Coupled Problems in Science and Engineering". CIMNE, 2011, p. 1107-1119. ISBN 978-84-89925-78-6.
ISBN978-84-89925-78-6
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