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Abstract

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 and used to offline train a multilayer feedforward neural network, supervised by an error-back propagation (BP) neural algorithm. Such an identification technique is then validated, to be described in the second part of this study.

Keywords

structural health monitoring artificial neural network composite laminates FEM simulation Lamb wave wavelet transform damage parameters database

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Lamb Wave Propagation-based Damage Identification for Quasi-isotropic CF/EP Composite Laminates Using Artificial Neural Algorithm: Part I - Methodology and Database Development

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