Structural damage detection using time series analysis and piezoelectries actuators and sensors / Detecção de danos estruturais usando analise de series temporais e atuadores e sensores piezeletricos

AUTOR(ES)
DATA DE PUBLICAÇÃO

2008

RESUMO

This work proposes a novel approach to detect and locate incipient damage in structures by using only acceleration responses and coupled piezoelectric actuators and sensors. Though the major focus in smart damage detection is given by on the monitoring of the electrical impedance in the frequency domain, the current contribution applies a novel technique based on time series analysis. Regressive models, such as AR-ARX, ARMA and ARMAX, are employed to extract a feature index using the linear prediction errors. The use of nonlinear prediction by using discrete-time Wiener/Volterra models expanded by Kautz filter is also investigated. In order to decide correctly whether damage exists or not, a set of unsurpervised statistical pattern recognition techniques, namely the fuzzy clustering and the statistical process control, are implemented. Several numerical and experimental tests are performed to illustrate and compare the methodology developed with classical approaches. The efficacy of the approach is demonstrated through these tests

ASSUNTO(S)

kautz-volterra series volterra structural health monitoring localização de falhas (engenharia) smart structures series de reconhecimento de padrões analise de series temporais - processamento de dados materiais inteligentes statistical pattern recognition time series analysis

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