Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/100918
Type: Artigo de periódico
Title: Automation In Fault Detection Using Neural Network And Model Updating
Author: Pereira J.A.
Lopes Jr. V.
Weber H.I.
Abstract: In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.
Editor: Braz Soc Mech Sci, Rio de Janeiro, Brazil
Rights: aberto
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0032678595&partnerID=40&md5=da822287fdff50e619cc2f1552b94df9
Date Issue: 1999
Appears in Collections:Unicamp - Artigos e Outros Documentos

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