Assessment of impact detection tchniques for aeronautical application: ANN vs. LSSVM

File Description SizeFormat 
Passive_SVM.pdfAccepted version4.8 MBAdobe PDFView/Open
Title: Assessment of impact detection tchniques for aeronautical application: ANN vs. LSSVM
Authors: Yue, N
Sharif Khodaei, Z
Item Type: Journal Article
Abstract: The Impact localisation in composite panels is assessed using two machine learning techniques: least square support vector machines (LSSVM) and artificial neural networks (ANN) with local strain signals from piezoelectric sensors. Sensor signals from impact experiments on a composite plate as well as signals simulated by a finite element model are used to train and test models. A comparative study shows that LSSVM achieves better accuracy than ANN on identifying location of impacts for a combination of large mass impact and small mass impact, in particular when less data is available for training which is more appropriate for real aeronautical application. Additionally, LSSVM is more capable of identifying new impact events which have not been considered in the training process.
Issue Date: 11-Oct-2016
Date of Acceptance: 17-Aug-2016
URI: http://hdl.handle.net/10044/1/39436
DOI: https://dx.doi.org/10.1142/S1756973716400059
ISSN: 1756-9745
Publisher: World Scientific Publishing
Journal / Book Title: Journal of Multiscale Modeling
Volume: 07
Copyright Statement: © 2016 World Scientific Publishing Europe Ltd.
Keywords: Science & Technology
Physical Sciences
Mathematics, Interdisciplinary Applications
Mathematics
Passive sensing
impact detection and characterization in composites
meta-model
ANN
SVM
SUPPORT VECTOR MACHINES
PIEZOELECTRIC STRAIN SENSORS
STIFFENED COMPOSITE PANELS
IDENTIFYING IMPACTS
NEURAL-NETWORK
SYSTEM
IDENTIFICATION
PLATES
PREDICTION
LOCATION
Publication Status: Published
Article Number: 1640005
Appears in Collections:Faculty of Engineering
Aeronautics



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons