Unsupervised Sparse Pattern Diagnostic of Defects with Inductive Thermography Imaging System

Bin Gao, Wai Lok Woo, Yunze He, Gui Yun Tian

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)
67 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)371-383
Number of pages13
JournalIEEE Transactions on Industrial Informatics
Issue number1
Early online date26 Oct 2015
Publication statusPublished - 1 Feb 2016


Dive into the research topics of 'Unsupervised Sparse Pattern Diagnostic of Defects with Inductive Thermography Imaging System'. Together they form a unique fingerprint.

Cite this