Journal
JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES
Volume 16, Issue 2, Pages 97-111Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1045389X05047599
Keywords
structural health monitoring; artificial neural network; composite laminates; FEM simulation; Lamb wave; wavelet transform; damage parameters database
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A guided Lamb wave-based damage identification scheme and an online structural health monitoring (online-SHM) system with an integrated piezoelectric actuator-sensor network are developed. The proposed methodology is applied to the quantitative diagnosis of through-hole-type defect in the CF-EP quasi-isotropic laminate with the aid of an artificial neural network algorithm. For this purpose, a variety of composite laminates with stochastic damages are considered, and the corresponding three-dimensional dynamic FEM simulations are conducted. To describe a Lamb wave excited by the PZT actuator, models for both the piezoelectric actuator and sensor coupled with the composite laminates are established. A wavelet transform-based signal processing package (SPP) is devised to purify the acquired wave signals, and further extract characteristics from the energy spectra of Lamb waves over the time-scale domain. A concept of 'digital damage fingerprints' is introduced, with which a damage parameters database (DPD) is constructed ana used to offline train a multilayer feedforward neural network, supervised by an error-backpropagation (BP) neural algorithm. Such an identification technique is then validated. to be described in the second part of this study.
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