Pattern recognition of guided waves for damage evaluation in bars

Liew, Chin Kian and Veidt, Martin (2009) Pattern recognition of guided waves for damage evaluation in bars. Pattern Recognition Letters, 30 3: 321-330. doi:10.1016/j.patrec.2008.10.001

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Author Liew, Chin Kian
Veidt, Martin
Title Pattern recognition of guided waves for damage evaluation in bars
Journal name Pattern Recognition Letters   Check publisher's open access policy
ISSN 0167-8655
1872-7344
Publication date 2009-02-01
Year available 2008
Sub-type Article (original research)
DOI 10.1016/j.patrec.2008.10.001
Volume 30
Issue 3
Start page 321
End page 330
Total pages 10
Editor G. Sanniti di Baja
Place of publication Netherlands, Amsterdam
Publisher Elsevier
Language eng
Subject C1
091308 Solid Mechanics
970109 Expanding Knowledge in Engineering
Abstract Guided waves damage identification in bars with neural networks acquires training data from simulation as a cost-effective measure. These neural networks applied with a novel test inputs dependent iterative training scheme are capable of quantifying damages accurately from experimental inputs. The reliability of the predictions depends on the quality of the measured signals, which can be increased by considering more than one signal obtained from different sensor locations or by changing the properties of the interrogation pulse. A parallel network system to process the inputs from these signals collaboratively is described. The core of the system is a data fusion process that associates overlapping intermediate test results while isolating outliers to narrow the training range for improved generalization in the iterative test inputs dependent training scheme. This robust system of signal processing has achieved accurate average damage quantitative results with errors below 4% and 13% the original size of the training parameter space for damage location and depth, respectively, of artificial laminar defects in bars.
Keyword Ultrasonics
Neural-network
quantitative nondestructive evaluation
Structural health monitoring
Multi-layer Perceptron
Ensemble networks
Modular networks
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mechanical & Mining Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 10 times in Scopus Article | Citations
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Created: Thu, 03 Sep 2009, 08:48:31 EST by Mr Andrew Martlew on behalf of School of Mechanical and Mining Engineering