A blind deconvolution method for terahertz thermal barrier coating adapted by novel window function
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1.School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China; 2.School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China; 3.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; 4.Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China

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TH744

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    Abstract:

    The seriously overlapped terahertz (THz) signals of thermal barrier coatings (TBCs) result in unrecognizable echoes and reduce the accuracy of thickness measurement. Therefore, the blind deconvolution method for THz thermal barrier coating adapted by the novel window function is proposed in this article. The similarity between the window function and the echo is enhanced to improve the reconstruction precision of the deconvolution signal. Firstly, the features of THz echo of TBCs are explored based on the analytical model. A novel window function is presented to improve the similarity between the window function and the THz echo by crosscorrelation theory and swarm intelligence algorithm. The FIR filter with the novel window function is used to separate the overlapped echoes. Secondly, the time of flight and refractive index are obtained by the first three echoes to calculate the thickness of TBCs, and the Kirchhoff approximation is employed to characterize the influence of the rough surface of TBCs, followed by correcting the refractive index to reduce the thickness measurement errors. Finally, experiments are implemented to evaluate the effectiveness of the proposed method. Compared with frequency wavelet domain deconvolution and the improved maximum correlated kurtosis deconvolution, the results show that the refractive index measurement accuracy of the proposed method is improved by 76.32% and 83.51%. The thickness measurement accuracy is improved by 76.20% and 89.67%, respectively.

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  • Received:
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  • Online: December 19,2024
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