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Non-destructive evaluation tool for monitoring and detection of structural damage by using Neural Network

dc.contributor.authorDemarchi, D. [UNESP]
dc.contributor.authorPereira e, J. A. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-28T18:54:30Z
dc.date.available2022-04-28T18:54:30Z
dc.date.issued2000-01-01
dc.description.abstractThis work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.en
dc.description.affiliationUNESP, Ilha Solteira
dc.description.affiliationUnespUNESP, Ilha Solteira
dc.identifier.citationProceedings of SPIE - The International Society for Optical Engineering, v. 4062.
dc.identifier.issn0277-786X
dc.identifier.scopus2-s2.0-0033877614
dc.identifier.urihttp://hdl.handle.net/11449/219233
dc.language.isoeng
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineering
dc.sourceScopus
dc.titleNon-destructive evaluation tool for monitoring and detection of structural damage by using Neural Networken
dc.typeTrabalho apresentado em evento

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