Multisource data fusion for classification of surface cracks in steel pipes

Mustapha, S; Braytee, A; Ye, L

Multisource data fusion for classification of surface cracks in steel pipes

Mustapha, S Braytee, A

Ye, L

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Publisher:: ASME International
Publication Type:: Journal Article
Citation:: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 2018, 1, (2)
Issue Date:: 2018-05-01

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Field	Value	Language
dc.contributor.author	Mustapha, S
dc.contributor.author	Braytee, A https://orcid.org/0000-0003-2561-6496
dc.contributor.author	Ye, L
dc.date.accessioned	2022-09-10T23:07:54Z
dc.date.available	2022-09-10T23:07:54Z
dc.date.issued	2018-05-01
dc.identifier.citation	Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 2018, 1, (2)
dc.identifier.issn	2572-3901
dc.identifier.issn	2572-3898
dc.identifier.uri	http://hdl.handle.net/10453/161628
dc.description.abstract	This paper focuses on the development and validation of a robust framework for surface crack detection and assessment in steel pipes based on measured vibration responses collected using a network of piezoelectric (PZT) wafers. The pipe structure considered in this study contained multiple progressive cracks occurring at different locations and with various orientations (along the circumference or length). The fusion of data collected from multiple PZT wafers was investigated based on two approaches: (a) combining the raw data from all sensors before establishing a statistical model for damage classification and (b) combining the features from each sensor after applying a multiclass support vector machine recursive feature elimination (MCSVM-RFE), for dimensionality reduction, and taking the union of discriminative features among the different sources of data. A MCSVM learning algorithm was employed to train the data and generate a statistical classifier. The dataset consisted of ten classes, consisting of nine damage cases and the healthy state. The accuracy of the prediction based on the two fusion approaches resulted in a high accuracy, exceeding 95%, but the number of features needed to enrich the accuracy (95%) differed between the two approaches. Furthermore, the performance and the precision in the prediction of the classifier were evaluated when the data from only a single sensor was used compared with the combined data from all the sensors within the network. Very promising results in the classification of damage were obtained, based on the case study that included multiple damage scenarios with different lengths and orientations.
dc.language	en
dc.publisher	ASME International
dc.relation.ispartof	Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
dc.relation.isbasedon	10.1115/1.4038862
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Multisource data fusion for classification of surface cracks in steel pipes
dc.type	Journal Article
utslib.citation.volume	1
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-09-10T23:07:52Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	1
utslib.citation.issue	2

Abstract:

This paper focuses on the development and validation of a robust framework for surface crack detection and assessment in steel pipes based on measured vibration responses collected using a network of piezoelectric (PZT) wafers. The pipe structure considered in this study contained multiple progressive cracks occurring at different locations and with various orientations (along the circumference or length). The fusion of data collected from multiple PZT wafers was investigated based on two approaches: (a) combining the raw data from all sensors before establishing a statistical model for damage classification and (b) combining the features from each sensor after applying a multiclass support vector machine recursive feature elimination (MCSVM-RFE), for dimensionality reduction, and taking the union of discriminative features among the different sources of data. A MCSVM learning algorithm was employed to train the data and generate a statistical classifier. The dataset consisted of ten classes, consisting of nine damage cases and the healthy state. The accuracy of the prediction based on the two fusion approaches resulted in a high accuracy, exceeding 95%, but the number of features needed to enrich the accuracy (95%) differed between the two approaches. Furthermore, the performance and the precision in the prediction of the classifier were evaluated when the data from only a single sensor was used compared with the combined data from all the sensors within the network. Very promising results in the classification of damage were obtained, based on the case study that included multiple damage scenarios with different lengths and orientations.

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http://hdl.handle.net/10453/161628