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Application of artificial neural networks for compounding multiple damage indices in Lamb‐wave‐based damage detection
Author(s) -
Dworakowski Ziemowit,
Ambrozinski Lukasz,
Packo Pawel,
Dragan Krzysztof,
Stepinski Tadeusz
Publication year - 2015
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.1659
Subject(s) - artificial neural network , lamb waves , classifier (uml) , structural health monitoring , engineering , artificial intelligence , ultrasonic sensor , piezoelectric sensor , pattern recognition (psychology) , acoustics , computer science , structural engineering , piezoelectricity , surface wave , telecommunications , physics , electrical engineering
SUMMARY This paper presents a novel approach to the problem of health monitoring of aircraft structures using Lamb waves. Piezoelectric sensors, embedded in the aircraft sheathing, generate Lamb waves with the aim to monitor the structural integrity of complex structure parts. The ultrasonic signals obtained from the sensor pairs arranged in pitch‐catch configuration are used for the calculation of a number of different damage indices. The damage indices are then used as inputs for a classifier employing an artificial neural network (ANN) that is trained to perform structure condition assessment. Efficiency of the ANN classifier trained on artificial data generated from the numerical simulations performed using linear interaction simulation approach is investigated. The resulting classification results are compared with those obtained for the ANN trained on experimental data from the real specimens. Copyright © 2014 John Wiley & Sons, Ltd.