基于改进自适应阈值EIT算法的CFRP损伤检测

马敏; 山雨泽

doi:10.3969/j.issn.1000-1158.2024.05.17

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计量学报 ›› 2024, Vol. 45 ›› Issue (5) : 730-737. DOI: 10.3969/j.issn.1000-1158.2024.05.17

无线电、时间频率计量

基于改进自适应阈值EIT算法的CFRP损伤检测

马敏,山雨泽

作者信息 +

Research on CFRP Damage Detection Based on Improved Adaptive Threshold EIT Algorithm

MA Min,SHAN Yuze

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摘要

电阻抗层析成像(EIT)具有快速、无辐射等众多优点,但EIT的逆问题严重的病态性导致FISTA等算法的重建图像中存在损伤边缘信息缺失的现象。针对该问题引入了一种与解向量稀疏度相关的自适应阈值算子和一种可变阈值函数,解决了软阈值函数边缘处不可导的问题。仿真实验表明改进算法与传统的算法相比,FIMSTA算法的表现最好,尤其是对于传统算法图像重建效果较差的中心裂纹损伤,FIMSTA算法的相关系数达到0.7038,较表现最好的FISTA算法提升了51.08%。

Abstract

Electrical impedance tomography (EIT) has many advantages such as fast speed and no radiation, and has gradually emerged in the research of carbon fiber reinforced composite materials in recent years. The serious pathological nature of the inverse problem of EIT leads to the phenomenon of missing damaged edge information in the reconstructed images of algorithms such as FISTA. An adaptive threshold operator related to the sparsity of the solution vector and a variable threshold function have been introduced to address the above issues, solving problem of non differentiability at the edges of the soft threshold function. The simulation experiment shows that compared with traditional algorithms, the improved algorithm performs the best overall. For the center crack damage with poor image reconstruction performance of traditional algorithms, the correlation coefficient of the FIMSTA algorithm reaches 0.7038, which is 51.08% higher than the best performing FISTA algorithm.

Key words

radio metrology / electrical impedance tomography / CFRP / damage detection / sparse regularization algorithm / image reconstruction / adaptive threshold operator / variable threshold function

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马敏,山雨泽. 基于改进自适应阈值EIT算法的CFRP损伤检测[J]. 计量学报. 2024, 45(5): 730-737 https://doi.org/10.3969/j.issn.1000-1158.2024.05.17

MA Min,SHAN Yuze. Research on CFRP Damage Detection Based on Improved Adaptive Threshold EIT Algorithm[J]. Acta Metrologica Sinica. 2024, 45(5): 730-737 https://doi.org/10.3969/j.issn.1000-1158.2024.05.17

中图分类号： 无线电计量电阻抗层析成像 CFRP 损伤检测稀疏正则化算法图像重建自适应阈值算子可变阈值函数

参考文献

［1］黄亿洲, 王志瑾, 刘格菲. 碳纤维增强复合材料在航空航天领域的应用［J］. 西安航空学院学报, 2021, 39 (5): 8.
HUANG Y Z, WANG Z J, LIU G F. The application of carbon fiber reinforced composite materials in the aerospace field ［J］. Journal of Xian Aviation University, 2021, 39 (5): 8.
［2］HELEN L, LUCIEN F, ZHEN J. Composite materials for primary aircraft structures: from development phase to high volume production rate ［J］. Design and research of civil aircraft, 2020, 136 (1): 125-128.
［3］骆心怡, 王开坤, 熊克, 等. 碳纤维复合材料单向层合板自传感特性［J］. 工程科学学报, 2002, 24 (6): 638-642.
LUO X Y, WANG K K, XIONG K, et al Self sensing characteristics of unidirectional carbon fiber composite laminates ［J］. Journal of Engineering Science, 2002, 24 (6): 638-642.
［4］TESINOVA P. Advances in Composite Materials—Analysis of Natural and Man-Made Materials［M］.Rijeka:InTech,2011.
PICHE A, REVEL I, PERES G.Experimental and Numerical Methods to Characterize Electrical Behaviour of Carbon Fiber Composites Used in Aeronautic Industry ［J］. InTech, 2011.DOI: 10.5772/17563.
［5］DJAJAPUTRA D. Electrical Impedance Tomography: Methods, History and Applications ［J］. Medical Physics, 2005, 32 (8):https://doi.org/10.1118/1.1995712.
［6］QI W, WANG H. Image reconstruction based on 11 regularization for electrical impedance tomography (EIT)［C］// 2011 IEEE International Instrumentation and Measurement Technology Conference.2011.
［7］FAN W, LI J, CUI Z, et al. Visual inspection of CFRP laminates based on EIT［C］// 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). 2019.
［8］马敏, 于洁, 范文茹.基于改进联合稀疏EIT算法的CFRP材料检测［J］. 北京航空航天大学学报, 2023, 49 (2): 265-272.
MA M, YU J, FAN W R. CFRP material detection based on improved joint sparse EIT algorithm ［J］. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49 (2): 265-272.
［9］CHANG T T, HUO X Y. Modified Newton Raphson Algorithm for Electrical Impedance Image Reconstruction［C］//Lecture Notes on Data Engineering and Communications Technologies. 2022.
［10］张胜男, 许燕斌, 董峰. 自适应阈值收缩算子的稀疏正则化图像重建算法［J］. 中国科学院大学学报, 2020, 37 (2): 242-247.
ZHANG S N, XU Y B, DONG F. Sparse regularization image reconstruction algorithm based on adaptive threshold shrinkage operator ［J］. Journal of University of Chinese Academy of Sciences, 2020, 37 (2): 242-247.
［11］ZHAO Q, ZHANG K, ZHU S, et al. Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer ［J］. Applied Sciences, 2019, 9 (11): 2390.
［12］李靓瑶. 基于电阻抗成像的CFRP层压板损伤检测研究［D］. 天津:中国民航大学, 2020.
［13］吴光文, 王昌明, 包建东, 等. 基于自适应阈值函数的小波阈值去噪方法［J］. 电子与信息学报, 2014, 36 (6): 1340-1347.
WU G W, WANG C G, BAO J D, et al.Wavelet threshold denoising method based on adaptive threshold function ［J］. Journal of Electronics and Information Technology, 2014, 36 (6): 1340-1347.
［14］BECK A, TEBOULLE M. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems ［J］. Siam J Imaging Sciences, 2009, 2 (1): 183-202.
［15］赵继印, 李先涛, 赵静荣, 等. 基于半软阈值法的图像小波去噪方法［J］. 大庆石油学院学报, 2004, 28 (1): 63-65.
ZHAO J.Y, LI X T, ZHAO J R, et a.l.Image wavelet denoising method based on semi soft threshold method ［J］. Journal of Daqing Petroleum Institute, 2004, 28 (1): 63-65.
［16］DAUBECHIES I, DEFRISE M, MOL C D. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint ［J］. Communications on Pure & Applied Mathematics, 2010, 57 (11): 1413-1457.
［17］包建文, 蒋诗才, 张代军.航空碳纤维树脂基复合材料的发展现状和趋势［J］. 科技导报, 2018, 36 (19): 52-63.
BAO J W, JIANG S C, ZHANG D J. Development Status and Trends of Aviation Carbon Fiber Resin Matrix Composite Materials ［J］. Science and Technology Bulletin, 2018, 36 (19): 52-63.
［18］DAUBECHIES I, DEFRISE M, MOL C D. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint ［J］. Communications on Pure & Applied Mathematics, 2010, 57 (11): 1413-1457.
［19］范文茹, 雷建, 董玉珊, 等. 基于四电极法的CFRP结构损伤检测研究［J］. 仪器仪表学报, 2017, 38 (4): 961-968.
FAN W R, LEI J, DONG Y S, et al.Research on damage detection of CFRP structures based on the four electrode method ［J］. Chinese Journal of Scientific Instrumentn, 2017, 38 (4): 961-968.
［20］马敏, 郭鑫, 于洁.改进正则化半阈值算法的ECT图像重建［J］. 仪器仪表学报, 2022, 43 (5): 110-119.
MA M, GUO X, YU J.ECT image reconstruction based on improved regularization half threshold algorithm ［J］. Chinese Journal of Scientific Instrument, 2022, 43 (5): 110-119.
［21］马敏, 郭鑫.基于改进SR3模型算法的ECT图像重建研究［J］. 计量学报, 2023, 44 (1): 95-102.
MA M, GUO X. Research on ECT image reconstruction based on improved SR3 model algorithm ［J］. Acta Metrologica Sinica, 2023, 44 (1): 95-102.
［22］马敏, 王涛.双小波非凸稀疏正则化去噪算法研究［J］. 计量学报, 2021, 42 (1): 85-90.
MA M, WANG T. Research on dual wavelet non convex sparse regularization denoising algorithm ［J］. Acta Metrologica Sinica, 2021, 42 (1): 85-90.