Comparative analysis on thermal non-destructive testing imagery applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT)

Yousefi, Bardia; Сфарра, Стефано; Ibarra-Castanedo, Clemente; Maldague, Xavier

doi:10.1016/j.infrared.2017.06.008

Cited by 96 publications

(58 citation statements)

References 16 publications

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“…Usually, an infrared sequence of 1000 images can be replaced by 10 or less EOF [34]. Another advantage of PCT is its suitability to be combined with other image processing techniques, e.g., in [35][36][37].…”

Section: Infrared Image Processingmentioning

confidence: 99%

Thermographic Non-Destructive Evaluation for Natural Fiber-Reinforced Composite Laminates

et al. 2018

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Natural fibers, including mineral and plant fibers, are increasingly used for polymer composite materials due to their low environmental impact. In this paper, thermographic non-destructive inspection techniques were used to evaluate and characterize basalt, jute/hemp and bagasse fibers composite panels. Different defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, homogeneous particleboards of sugarcane bagasse, a new plant fiber material, were studied. Pulsed phase thermography and principal component thermography were used as the post-processing methods. In addition, ultrasonic C-scan and continuous wave terahertz imaging were also carried out on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different methods was given.

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Section: Infrared Image Processingmentioning

confidence: 99%

Thermographic Non-Destructive Evaluation for Natural Fiber-Reinforced Composite Laminates

et al. 2018

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“…One of the important application of the proposed approach is to determine the better defect-representative eigenimages obtained by Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT) [24,25], Principal Component Thermography (PCT) [26], Non-negative Matrix Factorization (NMF) [27], etc. Eigen-image establishment is one of an important concept in using decomposition method and following this intriguing necessity fi nding the better defect representative images were required an expert manual interference.…”

Section: Application For Automatic Rank Matrix Determinationmentioning

confidence: 99%

Application of Deep Learning in Infrared Non-Destructive Testing

Yousefi¹,

Kalhor²,

Usamentiaga³

et al. 2018

Proceedings of the 2018 International Conference on Quantitative InfraRed Thermography

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Convolutional Neural Networks (CNN) is already known as strong tools in various fields particularly in image processing and computer vision. This paper aims to exploit the power of CNN for transform learning and utilizes it as an unsupervised feature extractor for analyzing defects in a steel specimen with float batten holes and Carbon Fiber Reinforced Plastic (CFRP) composite materials. A pre-trained CNN (ImageNet-VGG-f) has been used for extraction of the vectorized features along with a spectral angler mapper (SAM) to provide a score for defects presented in the image. Empirical results on two aforementioned datasets indicate a promising performance for application of heating and cooling based active thermography with a reasonable computational cost due to unsupervised nature of the algorithm.

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“…The theory and appropriate application procedures are well established and discussed in the literature (e.g., [18][19][20][21][22][23]). …”

Section: Principal Component Thermography Approachmentioning

confidence: 99%

Extraction of Independent Structural Images for Principal Component Thermography

Gavrilov

Maev

2018

Applied Sciences

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Thermography is a powerful tool for non-destructive testing of a wide range of materials. Thermography has a number of approaches differing in both experiment setup and the way the collected data are processed. Among such approaches is the Principal Component Thermography (PCT) method, which is based on the statistical processing of raw thermal images collected by thermal camera. The processed images (principal components or empirical orthogonal functions) form an orthonormal basis, and often look like a superposition of all possible structural features found in the object under inspection-i.e., surface heating non-uniformity, internal defects and material structure. At the same time, from practical point of view it is desirable to have images representing independent structural features. The work presented in this paper proposes an approach for separation of independent image patterns (archetypes) from a set of principal component images. The approach is demonstrated in the application of inspection of composite materials as well as the non-invasive analysis of works of art.

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Comparative analysis on thermal non-destructive testing imagery applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT)

Cited by 96 publications

References 16 publications

Thermographic Non-Destructive Evaluation for Natural Fiber-Reinforced Composite Laminates

Thermographic Non-Destructive Evaluation for Natural Fiber-Reinforced Composite Laminates

Application of Deep Learning in Infrared Non-Destructive Testing

Extraction of Independent Structural Images for Principal Component Thermography

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