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A comparison of artificial neural network learning algorithms for vibration-based damage detection

This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM),...

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Main Authors: Goh, Lyn Dee, Bakhary, Norhisham, Abdul Rahman, Azlan, Ahmad, Baderul Hisham
Format: Article
Published: Trans Tech Publications 2011
Subjects:
QA76 Computer software
Online Access:http://eprints.utm.my/id/eprint/44681/
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spelling my.utm.446812017-08-29T06:46:38Z http://eprints.utm.my/id/eprint/44681/ A comparison of artificial neural network learning algorithms for vibration-based damage detection Goh, Lyn Dee Bakhary, Norhisham Abdul Rahman, Azlan Ahmad, Baderul Hisham QA76 Computer software This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance. Trans Tech Publications 2011 Article PeerReviewed Goh, Lyn Dee and Bakhary, Norhisham and Abdul Rahman, Azlan and Ahmad, Baderul Hisham (2011) A comparison of artificial neural network learning algorithms for vibration-based damage detection. Advanced Materials Research, 163-16 . pp. 2756-2760. ISSN 1022-6680
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA76 Computer software
spellingShingle QA76 Computer software
Goh, Lyn Dee
Bakhary, Norhisham
Abdul Rahman, Azlan
Ahmad, Baderul Hisham
A comparison of artificial neural network learning algorithms for vibration-based damage detection
description This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance.
format Article
author Goh, Lyn Dee
Bakhary, Norhisham
Abdul Rahman, Azlan
Ahmad, Baderul Hisham
author_facet Goh, Lyn Dee
Bakhary, Norhisham
Abdul Rahman, Azlan
Ahmad, Baderul Hisham
author_sort Goh, Lyn Dee
title A comparison of artificial neural network learning algorithms for vibration-based damage detection
title_short A comparison of artificial neural network learning algorithms for vibration-based damage detection
title_full A comparison of artificial neural network learning algorithms for vibration-based damage detection
title_fullStr A comparison of artificial neural network learning algorithms for vibration-based damage detection
title_full_unstemmed A comparison of artificial neural network learning algorithms for vibration-based damage detection
title_sort comparison of artificial neural network learning algorithms for vibration-based damage detection
publisher Trans Tech Publications
publishDate 2011
url http://eprints.utm.my/id/eprint/44681/
_version_ 1643651512116707328
score 13.211869

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