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

Lu, Peng, Gao, Bin, Feng, Qizhi, Yang, Yang, Woo, Wai Lok and Tian, Gui Yun (2017) Ensemble variational Bayes tensor factorization for super resolution of CFRP debond detection. Infrared Physics & Technology, 85. pp. 335-346. ISSN 1350-4495

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Optical pulsed thermography, Debond defects, CFRP, Ensemble variational Bayes tensor factorization, Non-destructive testing
Subjects:	F300 Physics G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	27 Mar 2019 16:05
Last Modified:	10 Oct 2019 21:01
URI:	http://nrl.northumbria.ac.uk/id/eprint/38579

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