基于改进低秩稀疏正则化的CFRP电阻抗层析成像算法研究

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (14) : 151-157.

论文

马敏，于洁，范文茹

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CFRP electrical impedance tomography based on an improved low rank sparse regularization algorithm

MA Min,YU Jie,FAN Wenru

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文章历史 +

摘要

碳纤维复合材料（Carbon Fiber Reinforced Polymer， CFRP）由于其轻质高强、抗疲劳等优势被广泛应用于航空航天领域。为确保材料使用的安全性，碳纤维复合材料的有效检测尤为重要。近年来，电阻抗层析成像（Electrical Impedance Tomography，EIT）因其低成本、无辐射等优点已成为一种新兴的损伤监测方法并受到了广泛关注。针对电阻抗层析成像逆问题求解具有严重的病态性，提出了一种基于改进低秩稀疏正则化的电阻抗层析成像算法。首先，引入Lp伪范数，通过调节p的值来增强解的稀疏性、提高图像重建精度，其次，采用核范数作为解的低秩约束能有效利用先验信息提高重建质量，最后，通过分裂布雷格曼（Split Bregman）方法求解，增强算法的实时性，使成像速度保持在0.06s。仿真与实验结果表明，改进低秩稀疏正则化算法能有效改善电极伪影、呈现出更加清晰的损伤细节并且具有较强的鲁棒性，具有实效性和适用性。
关键词：碳纤维复合材料；电阻抗层析成像；低秩稀疏；分裂布雷格曼；损伤监测

Abstract

Carbon Fiber Reinforced Polymer (CFRP) has been widely used in aerospace field due to its advantages of light weight, high strength and fatigue resistance. In order to ensure the safety of the materials used, the effective detection of CFRP is particularly important. In recent years, Electrical Impedance Tomography (EIT) has become a new method for damage detection and has attracted wide attention due to its low cost and no radiation. Aming at the serious ill-posedness of inverse problem solving of EIT, an improved low-rank sparse regularization algorithm is proposed. Firstly, Lp pseudo-norm is introduced to enhance the sparsity of the solution and improve the reconstruction image accuracy by adjusting the value of p. Secondly, the low-rank constraint using the kernel norm can effectively use the prior information to improve the reconstruction image quality. Finally, the algorithm is solved by Split Bregman method to enhance the real-time performance of the algorithm and keep the imaging speed at 0.06s. Simulation and experimental results show that the improved low-rank sparse regularization algorithm can effectively improve the electrode artifacts, present clearer damage details, and has strong robustness, effectiveness and applicability.

导出引用

马敏，于洁，范文茹. 基于改进低秩稀疏正则化的CFRP电阻抗层析成像算法研究[J]. 振动与冲击, 2022, 41(14): 151-157

MA Min,YU Jie,FAN Wenru . CFRP electrical impedance tomography based on an improved low rank sparse regularization algorithm[J]. Journal of Vibration and Shock, 2022, 41(14): 151-157

参考文献

[1] 王惠芬,杨碧琦,刘刚.航天器结构材料的应用现状与未来展望[J].材料导报,2018,32(S1):395-399.
WANG Hui-fen,YANG Bi-qi,LIU Gang. Application status and future prospect of materials for spacecraft structures[J]. Materials Reports,2018,32(S1):395-399.
[2] Fan Wen-ru,Li Jing-yao,Wang Hua-xiang,et al.Visual inspection of CFRP laminates based on EIT[C]//2019 IEEE International Instrumentation and Measurement Technology Conference (I2M TC), Auckland, New Zealand,2019,1-5.
[3] Nguyen D,Abdullah M S B,Ryan K,et al.The effect of fiber orientation on tool wear in edge-trimming of carbon fiber reinforced plastics (CFRP) laminates[J].Wear,2020,450-451.
[4] 王飞.CFRP复合材料缺陷的红外雷达热波成像与层析检测研究[D].哈尔滨工业大学,2020.
WANG Fei. Research on infrared radar thermal wave imaging and tomography for detection of defects in CFRP composite[D]. Harbin Institute of Technology,2020.
[5] 王琦,彭圆圆,汪剑鸣,等.动态电阻抗成像时空相关性重建方法研究[J].电子测量与仪器学报,2018,32(02):153-160.
WANG Qi,PENG Yuan-yuan,WANG Jian-ming,et al. Research on spatio-temporal relativity reconstruction method in dynamic electrical impedance tomography[J].Journal of Electronic Meas-urement and Instrumentation,2018,32(02):153-160.
[6] Liu S,Huang Y,Wu H,et al.Efficient Multi-Task Structure-Aware Sparse Bayesian Learning for Frequency-Difference Electrical Impedance Tomography[J].IEEE Transactions on Industrial Informatics,2021,17(1):463-472.
[7] Wang H B,Xu G ZH,Zhang SH,et al.Optimized excitation mode for generalized back projection algorithm in 3-D EIT[J]. IEEE Transactions on Magnetics,2015,51(3):1-4.
？[8] 范文茹,王化祥,郝魁红.基于两步迭代TV正则化的电阻抗图像重建算法[J].仪器仪表学报,2012,33(3): 625-630.
FAN Wen-ru,WANG Hua-xiang,,HAO Kui-hong.Two-step iter-ative TV regularization algorithm for image reconstruction of electrical impedance tomography[J].Chinese Journal of Scientific Instrument,2012,33(3):625-630.
[9] 王超,王化祥.电阻抗断层图像重建算法研究——预迭代算法提出[J].信号处理,2002(06):547-550.
WANG Chao,WANG Hua-xiang.Research on the reconstruction algorithm of electrical impedance tomography—the pre-iteration reconstruction algorithm[J].Journal of Signal Processing,2002(06):547-550.
[10] Cui Z Q,Wang Q,Xue Q. A Review on Image Reconstruction Algorithms for Electrical Capacitance Resistance Tomography[J]. Sensor Review,2016,36(4):429-445.
[11] 马敏,刘一斐.基于改进半阈值算法的电容层析成像滑油监测方法研究[J].推进技术，2021，
MA Min,LIU Yi-fei.Research on Monitoring Method of Lubricating Oil Based on Improvement Semi-Threshold Algorithm of Electrical Capacitance Tomography[J]. Journal of Propulsion Technology，2021，
[12] 范文茹,王勃,李靓瑶,等.基于电阻抗层析成像的 CFRP 结构损伤检测[J].北京航空航天大学学报,2019,45(11):2177-2183.
FAN Wen-ru,WANG Bo,LI Jing-yao.Damage detection of CFRP structure based on electrical impedance tomography[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019,45(11):2177-2183.
[13] Hong T,Shi X P,Liang X S.Synthesis of Sparse linear array for directional modulation via convex optimization[J].IEEE Transactions on Antennas & Propagation,2018,66(8):3959- 3972.
[14] Wang L Z,Chen Y P,Lin F,et al.Impulse noise denoising using total variation with overlapping group sparsity and Lp-Pseudo-Norm shrinkage[J]. Applied Sciences, 2018, 8(11),2317.
?[15] Ye J,Wang H,Yang W.Image Recovery for electrical capacitan-ce tomography based on Low-Rank decomposition[J].IEEE Transactions on Instrumentation & Measurement,2017,66(7):1-9.
[16] Lai R,Li J.Manifold Based Low-Rank regularization for image restoration and semi-supervised learning[J].Journal of Scientific Computing,2017,74(3):1241-1263.
[17] Wang J,Ma J W,Han B,et al.Split Bregman iterative algorithm for sparse reconstruction of electrical impedance tomography[J].Signal Processing,2012,92(12):2952-2961.
[18] 成民民,戎舟,庞宗强.基于分裂Bregman方法的加权频差电阻抗成像算法[J].国外电子测量技术,2019,38(02):30-35.
CHENG Min-min,RONG Zhou,PANG Zong-qiang. Weighted frequency difference electrical impedance tomography algorithm based on split Bregman method[J].Foreign Electronic Measure-ment ?Technology,2019,38(02):30-35.
[19] Pratap B,Weldon W F.Eddy currents in anisotropic composites applied to pulsed machinery[J].IEEE Transactions on Magnetics,1996,32(2):437-444.
[20] 叶明,李晓丞,刘凯,等.一种基于U2-Net模型的电阻抗成像方法[J/OL].仪器仪表学报,2021,
YE Ming,LI Xiao-cheng,LIU Kai,et al. An image reconstruction method for electrical impedance tomography using U2-Net[J].Chinese Journal of Scientific Instrument,2021,
[21] Wright S J,Nowak R D,Figueiredo M A T.Sparse reconstruction by separableapproximation[J].IEEE Transactions on Signal Processing,2009,57(7):2479-2493.
[22] Lee J D,Sun Y,Saunders M.Proximal Newton-type methods for convex optimization[C]// Proceedings of the 25th International Conference on Neural Information Processing Systems.[S.l.]:NIPS,2012.
[23] YANG Yun-jie,Jia Jia-bin.An image reconstruction algorithm for electrical impedance tomography using adaptive group sparsity constraint[J].IEEE Transactions on Instrumentation and Measurement,2017,66(9):2295-2305.
[24]吴阳,刘凯,陈柏,等.自适应粒子群优化算法优化径向基函数神经网络用于电阻抗成像图像重建[J].仪器仪表学报,2020,41(06):240-249.
WU Yang,LIU Kai,CHEN Bai,et al. Image reconstruction for electrical impedance tomography using radial basis function neural network optimized with adaptive particle swarm optimization algorithm[J].Chinese Journal of Scientific Instrument, 2020,41(06):240-249.
[25] FAN Wen-ru,WANG Chi.A new damage estimation method for carbon fiber reinforced polymer based on electrical impedance tomography.[J].The Review of scientific instruments, 2021,92(2): 025102.