Ensemble Bayesian tensor factorization for debond thermal NDT

Peng Lu, Bin Gao, Qizhi Feng, Yang Yang, W. L. Woo, Jian Zhao, Xueshi Qiu, Liangyong Gu, Guiyun Tian

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of 2017 IEEE Far East NDT New Technology and Application Forum, FENDT 2017
PublisherIEEE
Pages228-232
Number of pages5
ISBN (Electronic)9781538616154, 9781538616130
ISBN (Print)9781538616161
DOIs
Publication statusPublished - 20 Dec 2018
Event2017 Far East NDT New Technology & Application Forum - Shaanxi Guesthouse, Xi’an, China
Duration: 22 Jun 201724 Jun 2017
http://www.fendti.com/2017/

Conference

Conference2017 Far East NDT New Technology & Application Forum
Abbreviated titleFENDT 2017
CountryChina
CityXi’an
Period22/06/1724/06/17
Internet address

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