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A New Approach Hybrid Based in Artificial Neural Networks to Detection and Classification of Failures in Aeronautical Structures

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Abstract:

In this paper presents a new hybrid methodology to perform fault detection and classification of aircraft structures using the tool as ARTMAP-Fuzzy and Perceptron multi-layer artificial neural networks. This method is divided into two steps, the first step performed by the multi-layer Perceptron neural network, which consists in the detection of abnormalities in the structure. The second step is performed by ARTMAP-Fuzzy neural network and consists of the classification of faults structural detected in the first time. The main application of this hybrid methodology is to assist in the inspection process of aeronautical structures in order to identify and characterize flaws as well, make decision-making in order to avoid accidents or air crashes. To evaluate this method, the modeling and simulation was carried out signals from a numerical model of an aluminum beam. The results obtained by the methodology demonstrating robustness and accuracy structural flaws.

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Periodical:

Applied Mechanics and Materials (Volume 763)

Pages:

175-181

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.763.175

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Online since:

May 2015

Authors:

Simone Silva Frutuoso Souza*, Fernando Parra dos Anjos Lima, Fábio Roberto Chavarette

Keywords:

Artificial Neural Networks, ARTMAP-Fuzzy, Hybrid Methodology, Multi-Layer Perceptron, Structural Health Monitoring

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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References

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