Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
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Data
2007-04-01
Autores
Silva, Samuel da
Dias Jr., Milton
Lopes Jr., Vicente [UNESP]
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Resumo
Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.
Descrição
Palavras-chave
Damage detection, Fuzzy c-means clustering, Principal component analysis, Structural health monitoring, Time series, Aerospace applications, Algorithms, Data compression, Fuzzy clustering, Mathematical models, Pattern recognition, Time series analysis, Vibration analysis, AR-ARX models, Damage sensitive index, Linear prediction
Como citar
Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 29, n. 2, p. 174-184, 2007.