Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/79191
Type: Artigo de periódico
Title: Structural damage detection by fuzzy clustering
Author: da Silva, S
Dias, M
Lopes, V
Brennan, MJ
Abstract: The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.
Subject: structural health monitoring
time series
principal component analysis
fuzzy clustering
Country: Inglaterra
Editor: Academic Press Ltd Elsevier Science Ltd
Rights: fechado
Identifier DOI: 10.1016/j.ymssp.2008.01.004
Date Issue: 2008
Appears in Collections:Unicamp - Artigos e Outros Documentos

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