Ensemble Bayesian tensor factorization for debond thermal NDT

Lu, P; Cao, B; Feng, Q; Yang, Y; Woo, WL; Zhao, J; Qiu, X; Gu, L; Tian, G

doi:10.1109/FENDT.2017.8584558

Ensemble Bayesian tensor factorization for debond thermal NDT

Lookup NU author(s): Dr Wai Lok Woo, Professor Gui Yun Tian ORCiD

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Abstract

© 2017 IEEE. 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.