Unsupervised Sparse Pattern Diagnostic of Defects With Inductive Thermography Imaging System

Gao, Bin, Woo, Wai Lok, He, Yunze and Tian, Gui Yun (2016) Unsupervised Sparse Pattern Diagnostic of Defects With Inductive Thermography Imaging System. IEEE Transactions on Industrial Informatics, 12 (1). pp. 371-383. ISSN 1551-3203

Gao et al - Unsupervised Sparse Pattern Diagnostic of Defects with Inductive Thermography Imaging System AAM.pdf - Accepted Version

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Official URL: https://doi.org/10.1109/TII.2015.2492925


This paper proposes an unsupervised method for diagnosing and monitoring defects in inductive thermography imaging system. The proposed method is fully automated and does not require manual selection from the user of the specific thermal frame images for defect diagnosis. The core of the method is a hybrid of physics-based inductive thermal mechanism with signal processing-based pattern extraction algorithm using sparse greedy-based principal component analysis (SGPCA). An internal functionality is built into the proposed algorithm to control the sparsity of SGPCA and to render better accuracy in sizing the defects. The proposed method is demonstrated on automatically diagnosing the defects on metals and the accuracy of sizing the defects. Experimental tests and comparisons with other methods have been conducted to verify the efficacy of the proposed method. Very promising results have been obtained where the performance of the proposed method is very near to human perception.

Item Type: Article
Uncontrolled Keywords: Data analytics for diagnosis and monitoring, instrumentation, inductive thermal imaging, machine intelligence, non-destructive testing and evaluation.
Subjects: G400 Computer Science
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 27 Feb 2019 13:13
Last Modified: 31 Jul 2021 13:36
URI: http://nrl.northumbria.ac.uk/id/eprint/38220

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