Crack detection using induction thermography during high-temperature testing

Genest, Marc; Dudzinski, D.; Dawag, Lesley; Kersey, Raymond K.

doi:10.1117/12.2015488

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chang et al [10] utilized the inductive thermography for metal specimen image damage detection and separation. For crack detection on metallic specimen, Genest et al [11] used the inductive thermography. Jackal and Netzelmann [12] utilized the IT to study the external influence of the magnetic field for the thermal contrast of crack type defects.…”

Section: Introductionmentioning

confidence: 99%

Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

Ahmed

Gao

Woo

2021

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

Ahmed

Gao

Woo

2021

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

“…Cheng et al [7] applied the IT to detect and separate the impact damage. Genest [8] used the IT to detect the crack defects. Netzelmann [9] utilized the IT to study the external influence of the magnetic field for the thermal contrast of crack type defects.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical low-rank and sparse tensor micro defects decomposition by electromagnetic thermography imaging system

Gao

Woo

2020

Phil. Trans. R. Soc. A.

View full text Add to dashboard Cite

With the advancement of electromagnetic induction thermography and imaging technology in non-destructive testing field, this system has significantly benefitted modern industries in fast and contactless defects detection. However, due to the limitations of front-end hardware experimental equipment and the complicated test pieces, these have brought forth new challenges to the detection process. Making use of the spatio-temporal video data captured by the thermal imaging device and linking it with advanced video processing algorithm to defects detection has become a necessary alternative way to solve these detection challenges. The extremely weak and sparse defect signal is buried in complex background with the presence of strong noise in the real experimental scene has prevented progress to be made in defects detection. In this paper, we propose a novel hierarchical low-rank and sparse tensor decomposition method to mine anomalous patterns in the induction thermography stream for defects detection. The proposed algorithm offers advantages not only in suppressing the interference of strong background and sharpens the visual features of defects, but also overcoming the problems of over- and under-sparseness suffered by similar state-of-the-art algorithms. Real-time natural defect detection experiments have been conducted to verify that the proposed algorithm is more efficient and accurate than existing algorithms in terms of visual presentations and evaluation criteria. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

show abstract

Review of conventional and advanced non-destructive testing techniques for detection and characterization of small-scale defects

Silva

Malitckii

Santos

et al. 2023

Progress in Materials Science

View full text Add to dashboard Cite

Crack detection using induction thermography during high-temperature testing

Cited by 4 publications

References 0 publications

Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

Hierarchical low-rank and sparse tensor micro defects decomposition by electromagnetic thermography imaging system

Review of conventional and advanced non-destructive testing techniques for detection and characterization of small-scale defects

Contact Info

Product

Resources

About