Tensor robust principal component analysis based on Bayesian Tucker decomposition for thermographic inspection

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dc.contributor.authorHu, Yue
dc.contributor.authorCui, Fangsen
dc.contributor.authorZhao, Yifan
dc.contributor.authorLi, Fucai
dc.contributor.authorCao, Shuai
dc.contributor.authorXuan, Fu-zhen
dc.date.accessioned2023-09-18T09:33:04Z
dc.date.available2023-09-18T09:33:04Z
dc.date.freetoread2024-09-15
dc.date.issued2023-09-06
dc.description.abstractThermographic inspection is considered an effective and promising nondestructive testing tool because of its intuitiveness, wide range and noncontact property. Despite this, the detection of weak defects and the recovery of their shape remain difficult, particularly when the surface being inspected is the opposite of the surface being drilled. This study proposes a new tensor robust principal component analysis method based on Bayesian Tucker decomposition to improve the spatial resolution of thermography. A hierarchical form of a generalized Student-t prior is imposed on the model parameters in the Bayesian framework so as to approximate the low-rank component related to the defect feature. Through variational Bayesian inference, all model parameters are adaptively estimated. Based on two experimental data, it appears that the proposed method is capable of improving the spatial resolution and detection accuracy of the thermographic inspection system.en_UK
dc.identifier.citationHu Y, Cui F, Zhao Y, et al., (2023) Tensor robust principal component analysis based on Bayesian Tucker decomposition for thermographic inspection, Mechanical Systems and Signal Processing, Volume 204, December 2023, Article Number 110761en_UK
dc.identifier.eissn1096-1216
dc.identifier.issn0888-3270
dc.identifier.urihttps://doi.org/10.1016/j.ymssp.2023.110761
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20225
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectTensor robust principal component analysisen_UK
dc.subjectBayesian Tucker decompositionen_UK
dc.subjectThermographic inspectionen_UK
dc.subjectNondestructive testingen_UK
dc.subjectGeneralized student-t distributionen_UK
dc.titleTensor robust principal component analysis based on Bayesian Tucker decomposition for thermographic inspectionen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2023-09-06

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