Ensemble Bayesian Tensor Factorization for Debond Thermal NDT

Lu, Peng, Cao, Bin, Feng, Qizhi, Yang, Yang, Woo, Wai Lok, Zhao, Jian, Qiu, Xueshi, Gu, Liangyong and Tian, Guiyun (2018) Ensemble Bayesian Tensor Factorization for Debond Thermal NDT. In: 2017 Far East NDT New Technology & Application Forum, 22nd - 24th June 2017, Xi’an, China.

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/FENDT.2017.8584558


One of the common types of defects in the carbon fiber reinforced polymer (CFRP) is debond. The different feature extraction algorithms of optical stimulated infrared thermography are used to obtained the debond detection. However, the low detection accuracy as well as remain as challenges. In this paper, the ensemble variational Bayes tensor factorization (EVBTF) has been proposed to overcome the problems. The framework of the proposed algorithm is based on the Bayesian learning theory. It constructs spatial-transient multi-layer mining structure. Experimental tests have been proved that it can effectively improve the contrast ratio between the defective areas and the sound areas.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: CFRP, Debond defects, Non-destructive testing, Tensor factorization
Subjects: G400 Computer Science
J400 Polymers and Textiles
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 26 Mar 2019 10:38
Last Modified: 10 Oct 2019 21:02
URI: http://nrl.northumbria.ac.uk/id/eprint/38543

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