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http://hdl.handle.net/10773/14915
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Almeida, Juliana | pt |
dc.contributor.author | Alonso, Hugo | pt |
dc.contributor.author | Ribeiro, Pedro | pt |
dc.contributor.author | Rocha, Paula | pt |
dc.date.accessioned | 2015-12-01T17:12:05Z | - |
dc.date.available | 2015-12-01T17:12:05Z | - |
dc.date.issued | 2015-10 | - |
dc.identifier.issn | 1573-0824 | pt |
dc.identifier.uri | http://hdl.handle.net/10773/14915 | - |
dc.description.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. | pt |
dc.language.iso | eng | pt |
dc.publisher | Springer | pt |
dc.relation | UID/MAT/04106/2013 | pt |
dc.relation | PTDC/EEA-AUT/108180/2008 | pt |
dc.relation | FCOMP-01-0124-FEDER-009842 | pt |
dc.rights | openAccess | por |
dc.subject | 2D Hopfield Neural Network | pt |
dc.subject | Euler-Bernoulli beam model | pt |
dc.subject | Timoshenko beam model | pt |
dc.subject | Damage detection | pt |
dc.title | A 2D Hopfield Neural Network approach to mechanical beam damage detection | pt |
dc.type | article | pt |
dc.peerreviewed | yes | pt |
ua.distribution | international | pt |
degois.publication.firstPage | 1081 | pt |
degois.publication.issue | 4 | pt |
degois.publication.lastPage | 1095 | pt |
degois.publication.title | Multidimensional Systems and Signal Processing | pt |
degois.publication.volume | 26 | pt |
dc.identifier.doi | 10.1007/s11045-015-0342-7 | pt |
Appears in Collections: | CIDMA - Artigos SCG - Artigos |
Files in This Item:
File | Description | Size | Format | |
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MULT-D-15-00043.pdf | 761.15 kB | Adobe PDF | View/Open |
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