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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Research-Article
Multisource Data Fusion for Classification of Surface Cracks in Steel Pipes
Samir Mustapha,
Samir Mustapha
Professor
Department of Mechanical Engineering,
Maroun Semaan Faculty of
Engineering and Architecture,
American University of Beirut,
Beirut 1107 2020, Lebanon;
Department of Mechanical Engineering,
Maroun Semaan Faculty of
Engineering and Architecture,
American University of Beirut,
Beirut 1107 2020, Lebanon;
Laboratory of Smart Materials and
Structures (LSMS),
School of Aerospace, Mechanical and
Mechatronic Engineering,
The University of Sydney,
Sydney 2006, New South Wales, Australia
e-mail: sm154@aub.edu.lb
Structures (LSMS),
School of Aerospace, Mechanical and
Mechatronic Engineering,
The University of Sydney,
Sydney 2006, New South Wales, Australia
e-mail: sm154@aub.edu.lb
Search for other works by this author on:
Ali Braytee,
Ali Braytee
Quantum Computation and Intelligent Systems,
University of Technology Sydney,
Sydney 2007, Australia
e-mail: ali.braytee@uts.edu.au
University of Technology Sydney,
Sydney 2007, Australia
e-mail: ali.braytee@uts.edu.au
Search for other works by this author on:
Lin Ye
Lin Ye
Professor
Laboratory of Smart Materials and
Structures (LSMS),
School of Aerospace, Mechanical and
Mechatronic Engineering,
The University of Sydney,
Sydney 2006, New South Wales, Australia
e-mail: lin.ye@sydney.edu.au
Laboratory of Smart Materials and
Structures (LSMS),
School of Aerospace, Mechanical and
Mechatronic Engineering,
The University of Sydney,
Sydney 2006, New South Wales, Australia
e-mail: lin.ye@sydney.edu.au
Search for other works by this author on:
Samir Mustapha
Professor
Department of Mechanical Engineering,
Maroun Semaan Faculty of
Engineering and Architecture,
American University of Beirut,
Beirut 1107 2020, Lebanon;
Department of Mechanical Engineering,
Maroun Semaan Faculty of
Engineering and Architecture,
American University of Beirut,
Beirut 1107 2020, Lebanon;
Laboratory of Smart Materials and
Structures (LSMS),
School of Aerospace, Mechanical and
Mechatronic Engineering,
The University of Sydney,
Sydney 2006, New South Wales, Australia
e-mail: sm154@aub.edu.lb
Structures (LSMS),
School of Aerospace, Mechanical and
Mechatronic Engineering,
The University of Sydney,
Sydney 2006, New South Wales, Australia
e-mail: sm154@aub.edu.lb
Ali Braytee
Quantum Computation and Intelligent Systems,
University of Technology Sydney,
Sydney 2007, Australia
e-mail: ali.braytee@uts.edu.au
University of Technology Sydney,
Sydney 2007, Australia
e-mail: ali.braytee@uts.edu.au
Lin Ye
Professor
Laboratory of Smart Materials and
Structures (LSMS),
School of Aerospace, Mechanical and
Mechatronic Engineering,
The University of Sydney,
Sydney 2006, New South Wales, Australia
e-mail: lin.ye@sydney.edu.au
Laboratory of Smart Materials and
Structures (LSMS),
School of Aerospace, Mechanical and
Mechatronic Engineering,
The University of Sydney,
Sydney 2006, New South Wales, Australia
e-mail: lin.ye@sydney.edu.au
1Corresponding author.
Manuscript received September 9, 2017; final manuscript received December 20, 2017; published online January 24, 2018. Assoc. Editor: Hoon Sohn.
ASME J Nondestructive Evaluation. May 2018, 1(2): 021007-021007-11 (11 pages)
Published Online: January 24, 2018
Article history
Received:
September 21, 2017
Revised:
December 20, 2017
Citation
Mustapha, S., Braytee, A., and Ye, L. (January 24, 2018). "Multisource Data Fusion for Classification of Surface Cracks in Steel Pipes." ASME. ASME J Nondestructive Evaluation. May 2018; 1(2): 021007–021007–11. https://doi.org/10.1115/1.4038862
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