A Comparison of Artificial Neural Network Learning Algorithms for Vibration-Based Damage Detection

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 163-167)

Edited by:

Lijuan Li

Pages:

2756-2760

DOI:

10.4028/www.scientific.net/AMR.163-167.2756

Citation:

G. Lyn Dee et al., "A Comparison of Artificial Neural Network Learning Algorithms for Vibration-Based Damage Detection", Advanced Materials Research, Vols. 163-167, pp. 2756-2760, 2011

Online since:

December 2010

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Price:

$35.00

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