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Feature Detection of GFRP Subsurface Defects Using Fast Randomized Sparse Principal Component Thermography

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Abstract

Machine learning plays an increasingly important role in the non-destructive testing and evaluation of composites. Principal component thermography (PCT) is often used as a popular feature extraction and dimension reduction method of thermographic data. However, the ability of PCT to handle large-scale thermographic data affects the applicability of PCT for high-resolution thermographic NDT. In this paper, fast randomized sparse principal component thermography (FRSPCT) is used to detect subsurface defects of glass fiber reinforced polymer (GFRP) composites. The effectiveness of the method is demonstrated by large-scale thermographic data of GFRP with subsurface defects. Furthermore, a comprehensive quantitative comparison of the method and other state-of-the-art methods was also conducted with respect to the signal-to-noise ratio, detection rate, and runtime. The comparison results show that the FRSPCT method gives the overall highest signal-to-noise ratio, detection rate, and satisfactory runtime. In addition, the method provides more easily interpretable defect detection results and highlights the hidden details of irregularly-shaped abnormal defects.

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The data that support the findings of this study are available from the corresponding authors upon reasonable request.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 11874110), the Key R&D Program of Jiangsu Province of China (Grant No. BE2021084), the Technical Security Project of China Market Supervision Administration in 2022 (2022YJ11), and the Scientific Research Foundation of Graduate School of Southeast University (Grant No. YBPY2005).

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  1. Peng Shen and Zhitao Luo have contributed equally to this work.

Authors and Affiliations

  1. Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast University, Nanjing, 211189, China

    Peng Shen, Zhitao Luo, Sheng Wang, Feilong Mao, Zihao Su & Hui Zhang

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  1. Peng Shen
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Shen, P., Luo, Z., Wang, S. et al. Feature Detection of GFRP Subsurface Defects Using Fast Randomized Sparse Principal Component Thermography. Int J Thermophys 43, 160 (2022). https://doi.org/10.1007/s10765-022-03076-z

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