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Transient-spatial pattern mining of eddy current pulsed thermography using wavelet transform

An Erratum to this article was published on 12 December 2014

Abstract

Eddy current pulsed thermography(ECPT) is an emerging Non-destructive testing and evaluation(NDT & E) technique, which uses hybrid eddy current and thermography NDT & E techniques that enhances the detectability from their compensation. Currently, this technique is limited by the manual selection of proper contrast frames and the issue of improving the efficiency of defect detection of complex structure samples remains a challenge. In order to select a specific frame from transient thermal image sequences to maximize the contrast of thermal variation and defect pattern from complex structure samples, an energy driven approach to compute the coefficient energy of wavelet transform is proposed which has the potential of automatically selecting both optimal transient frame and spatial scale for defect detection using ECPT. According to analysis of the variation of different frequency component and the comparison study of the detection performance of different scale and wavelets, the frame at the end of heating phase is automatically selected as an optimal transient frame for defect detection. In addition, the detection capabilities of the complex structure samples can be enhanced through proper spatial scale and wavelet selection. The proposed method has successfully been applied to low speed impact damage detection of carbon fibre reinforced polymer(CFRP) composite as well as providing the guidance to improve the detectability of ECPT technique.

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Authors

Corresponding author

Correspondence to Bin Gao.

Additional information

Supported by National Natural Science Foundation of China(Grant No. 51377015), China Post Doctor Project(Grant No. 136413), and Science & Technology Department of Sichuan Province, China(Grant No. 2013HH0059)

YANG Hailong, born in 1988, is currently a master candidate at School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China. His research interests include instrument science and non-destructive testing and evaluation.

GAO Bin, born in 1983, is currently an associate professor at School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China. He received his PhD degree from Newcastle University. His main research interests include sensors, signal processing, machine learning, data mining for non-destructive testing and evaluation.

TIAN Guiyun, born in 1965, is currently a professor and a PhD candidate supervisor at School of Electrical and Electronic Engineering, Newcastle University, UK. He received his PhD degree from University of Derby, Derby, UK, in 1998. Currently, he is “The Thousand Talents” professor with the School of Automation Engineering, University of Electronic Science and Technology of China. His mainly research interests include electromagnetic sensors, sensor array and sensor network, electromagnetic non-destructive testing and evaluation, advanced signal processing monitoring systems and applications.

REN Wenwei, born in 1968, is currently a teacher at School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China. Her main research interests include material science and non-destructive testing and evaluation.

WOO Wailok, born in 1972, is currently an associate professor and a PhD candidate supervisor at School of Electrical and Electronic Engineering, Newcastle University, UK. He is the director of Operations for the International Branch of the University, and head of the Machine Learning Centre for Sensing and Signal Processing. His research interests include intelligent systems and algorithms, sensing and signal processing for non-destructive testing and evaluation.

An erratum for this article can be found at http://dx.doi.org/10.1007/BF03396037

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Yang, H., Gao, B., Tian, G. et al. Transient-spatial pattern mining of eddy current pulsed thermography using wavelet transform. Chin. J. Mech. Eng. 27, 768–778 (2014). https://doi.org/10.3901/CJME.2014.0526.100

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Keywords

  • non-destructive testing and evaluation
  • composite impact damage detection
  • wavelet transform
  • energy driven approach
  • transient-spatial analysis