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

Lookup NU author(s): Dr Bin Gao, Yang Yang, Dr Wai Lok Woo, Professor Gui Yun TianORCiD

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Abstract

© 2017 Elsevier B.V. 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.

Publication metadata

Author(s): Lu P, Gao B, Feng Q, Yang Y, Woo WL, Tian GY

Publication type: Article

Publication status: Published

Journal: Infrared Physics and Technology

Year: 2017

Volume: 85

Pages: 335-346

Print publication date: 01/09/2017

Online publication date: 14/07/2017

Acceptance date: 10/07/2017

ISSN (print): 1350-4495

Publisher: Elsevier B.V.

URL: https://doi.org/10.1016/j.infrared.2017.07.012

DOI: 10.1016/j.infrared.2017.07.012

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