Ensemble variational Bayes tensor factorization for super resolution of CFRP debond detection

Peng Lu, Bin Gao, Qizhi Feng, Yang Yang, W. L. Woo, Gui Yun Tian

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


The carbon fiber reinforced polymer (CFRP) is widely used in aircraft and wind turbine blades. The common type of CFRP defect is debond. Optical pulse thermographic nondestructive evaluation (OPTNDE) and relevant thermal feature extraction algorithms are generally used to detect the debond. However, the resolution of detection performance remain as challenges. In this paper, the ensemble variational Bayes tensor factorization has been proposed to conduct super resolution of the debond detection. The algorithm is based on the framework of variational Bayes tensor factorization and it constructs spatial-transient multi-layer mining structure which can significantly enhance the contrast ratio between the defective regions and sound regions. In order to quantitatively evaluate the results, the event based F-score is computed. The different information regions of the extracted thermal patterns are considered as different events and the purpose is to objectively evaluate the detectability for different algorithms. Experimental tests and comparative studies have been conducted to prove the efficacy of the proposed method.
Original languageEnglish
Pages (from-to)335-346
Number of pages12
JournalInfrared Physics and Technology
Early online date14 Jul 2017
Publication statusPublished - Sept 2017


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