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 A 2D Hopfield Neural Network approach to mechanical beam damage detection
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/14915

title: A 2D Hopfield Neural Network approach to mechanical beam damage detection
authors: Almeida, Juliana
Alonso, Hugo
Ribeiro, Pedro
Rocha, Paula
keywords: 2D Hopfield Neural Network
Euler-Bernoulli beam model
Timoshenko beam model
Damage detection
issue date: Oct-2015
publisher: Springer
abstract: The aim of this paper is to present a method based on a 2D Hopfield Neural Network for online damage detection in beams subjected to external forces. The underlying idea of the method is that a significant change in the beam model parameters can be taken as a sign of damage occurrence in the structural system. In this way, damage detection can be associated to an identification problem. More concretely, a 2D Hopfield Neural Network uses information about the way the beam vibrates and the external forces that are applied to it to obtain time-evolving estimates of the beam parameters at the different beam points. The neural network organizes its input information based on the Euler-Bernoulli model for beam vibrations. Its performance is tested with vibration data generated by means of a different model, namely Timonshenko's, in order to produce more realistic simulation conditions.
URI: http://hdl.handle.net/10773/14915
ISSN: 1573-0824
publisher version/DOI: http://dx.doi.org/10.1007/s11045-015-0342-7
source: Multidimensional Systems and Signal Processing
appears in collectionsCIDMA - Artigos

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