留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于改进联合稀疏EIT算法的CFRP材料检测

马敏 于洁 范文茹

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 doi: 10.13700/j.bh.1001-5965.2021.0244
引用本文: 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 doi: 10.13700/j.bh.1001-5965.2021.0244
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 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0244
Citation: 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 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0244

基于改进联合稀疏EIT算法的CFRP材料检测

doi: 10.13700/j.bh.1001-5965.2021.0244
基金项目: 国家自然科学基金(61871379); 天津市教委科研计划(2020KJ012)
详细信息
    作者简介:

    马敏等:基于改进联合稀疏EIT算法的CFRP材料监测研究 9

    通讯作者:

    E-mail:mm5739@163.com

  • 中图分类号: TP212

CFRP material detection based on improved joint sparse EIT algorithm

Funds: National Natural Science Foundation of China (61871379); Scientific Research Program of Tianjin Education Commission (2020KJ012)
More Information
  • 摘要:

    针对应用电阻抗层析成像技术(EIT)对碳纤维增强复合材料(CFRP)损伤检测的高度病态性问题,提出了一种联合L1L2范数的稀疏正则化泛函模型,并在迭代过程中通过构建一种新的约束项来优化求解。仿真结果表明,与传统算法相比,改进联合稀疏EIT算法能够有效改善损伤图像的电极伪影,提高损伤边缘清晰度,增强损伤辨识定位准确度。CFRP层压板检测实验结果表明,改进联合稀疏EIT算法能够提高图像重建的抗干扰能力,具有良好的鲁棒性及适用性。

     

  • 图 1  CFRP模型

    Figure 1.  Model of CFRP

    图 2  EIT系统组成

    Figure 2.  EIT system composition

    图 3  改进联合稀疏EIT算法迭代流程

    Figure 3.  Improved iterative process of joint sparse EIT algorithm

    图 4  图像重建结果对比

    Figure 4.  Comparison of image reconstruction results

    图 5  相关系数对比

    Figure 5.  Comparison of correlation coefficient

    图 6  面积误差对比

    Figure 6.  Comparison of area error

    图 7  有无噪声情况下的成像效果对比

    Figure 7.  Comparison of imaging effects with or without noise

    图 8  有无噪声情况下相关系数对比

    Figure 8.  Comparison of correlation coefficient with or without noise

    图 9  CFRP层压板EIT损伤检测实验平台

    Figure 9.  EIT experimental platform for damage detection of CFRP composites

    图 10  CFRP 损伤检测成像效果对比

    Figure 10.  Comparison of imaging results of CFRP damage detection

    表  1  CFRP损伤模型

    Table  1.   CFRP damage model

    模型12345
    图示
    下载: 导出CSV
  • [1] 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. Current status and trends of aeronautical resin matrix composites reinforced by carbon fiber[J]. Science & Technology Review, 2018, 36(19): 52-63(in Chinese).
    [2] 王军照. 碳纤维复合材料在航空领域中的应用现状及改进[J]. 今日制造与升级, 2020(8): 48-49.

    WANG J Z. Application status and improvement of carbon fiber composites in aviation field[J]. Manufacture & Upgrading Today, 2020(8): 48-49(in Chinese).
    [3] 黄梅. 纤维增强复合材料的冲击损伤识别研究[D]. 广州: . 广州大学, 2020: 1-6.

    HUANG M. Study on impact damage identification of fiber reinforced composite[D]. Guangzhou: Guangzhou University, 2020: 1-6(in Chinese).
    [4] 张海燕, 宋佳昕, 任燕, 等. 碳纤维增强复合材料褶皱缺陷的超声成像[J]. 物理学报, 2021, 70(11): 114301. doi: 10.7498/aps.70.20210032

    ZHANG H Y, SONG J X, REN Y, et al. Ultrasonic imaging of wrinkles in carbon-fiber-reinforce-polymer composites[J]. Acta Physica Sinica, 2021, 70(11): 114301(in Chinese). doi: 10.7498/aps.70.20210032
    [5] 王从科, 董方旭, 赵付宝, 等. 碳纤维树脂基复合材料内部缺陷X射线成像检测的仿真[J]. 复合材料学与工程, 2017(2): 82-87.

    WANG C K, DONG F X, ZHAO F B, et al. The simulation of X-ray imaging detection of defects in carbon fiber resin composite material[J]. Composites Science and Engineering, 2017(2): 82-87(in Chinese).
    [6] 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: 哈尔滨工业大学, 2020: 62-81.

    WANG F. Research on inerared radar thermal wave imaging and tomography for detection of defects in CFRP composite[D]. Harbin: Harbin Institute of Technology, 2020: 62-81(in Chinese).
    [7] SCHUELER R P, JOSHI S, SCHULTE K. Damage detection in CFRP by electrical conductivity mapping[J]. Composites Science and Technology, 2001, 61(6): 921-983. doi: 10.1016/S0266-3538(00)00178-0
    [8] KHALED A, ARIEF Y, GILLES L. On the anisotropic behavior of electrodes for electrical-based monitoring of CFRP laminated composites[J]. Polymer Composites, 2019, 40(5): 2061-2066. doi: 10.1002/pc.24987
    [9] 范文茹, 王勃, 周琛. 基于EIT的CFRP层合板缺陷可视化检测[J]. 传感器与微系统, 2020, 39(2): 144-147. doi: 10.13873/j.1000-9787(2020)02-0144-04

    FAN W R, WANG B, ZHOU C. Visualization detection of CFRP laminate defects based on EIT[J]. Transducer and Microsystem Technologies, 2020, 39(2): 144-147(in Chinese). doi: 10.13873/j.1000-9787(2020)02-0144-04
    [10] 王琦, 张鹏程, 王化祥, 等. 基于块稀疏的电阻抗成像算法[J]. 电子与信息学报, 2018, 40(3): 676-682. doi: 10.11999/JEIT170425

    WANG Q, ZHANG P C, WANG H X, et al. Block-sparse reconstruction for electrical impedance tomography[J]. Journal of Electronics & Information Technology, 2018, 40(3): 676-682(in Chinese). doi: 10.11999/JEIT170425
    [11] WANG Q, WANG H, ZHANG R, et al. Image reconstruction based on L1 regularization and projection methods for electrical impedance tomography[J]. Review of Scientific Instruments, 2012, 83(10): 104707. doi: 10.1063/1.4760253
    [12] SOUVIK R, ALFIO B. A new optimization approach to sparse reconstruction of log-conductivity in acousto-electric tomography[J]. SIAM Journal on Imaging Science, 2018, 11(2): 1759-1784. doi: 10.1137/17M1148451
    [13] 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(2): 30-35. doi: 10.19652/j.cnki.femt.1801156

    CHENG M M, RONG Z, PANG Z Q. Weighted frequency difference electrical impedance tomography algorithm based on split Bregman method[J]. Foreign Electronic Measurement Technology, 2019, 38(2): 30-35(in Chinese). doi: 10.19652/j.cnki.femt.1801156
    [14] 范文茹, 李靓瑶, 王勃. 基于改进MRNSD算法的电阻抗层析成像[J]. 北京航空航天大学学报, 2020, 46(8): 1564-1573. doi: 10.13700/j.bh.1001-5965.2019.0504

    FAN W R, LI J Y, WANG B. Electrical impedance tomography based on improved MRNSD algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(8): 1564-1573(in Chinese). doi: 10.13700/j.bh.1001-5965.2019.0504
    [15] LIKASSA H T, FANG W H, LEU J S. Robust image recovery via affine transformation and L2,1 norm[J]. IEEE Access, 2019, 7: 125011-125021. doi: 10.1109/ACCESS.2019.2932470
    [16] LIU J J, NI A Q, NI G X. A nonconvex L1(L1-L2) model for image restoration with impulse noise[J]. Journal of Computational and Applied Mathematics, 2020, 378: 112934. doi: 10.1016/j.cam.2020.112934
    [17] HE B S, YUAN X M. On the O(1/n) convergence rate of the douglas-rachford alternating direction method[J]. SIAM Journal on Numerical Analysis, 2012, 50(2): 700-709. doi: 10.1137/110836936
    [18] 徐帅, 程军, 杨继全, 等. 各向异性碳纤维复合材料的方向性涡流检测[J]. 振动、测试与诊断, 2012, 50(2): 700-709. doi: 10.16450/j.cnki.issn.1004-6801.2019.03.026

    XU S, CHENG J, YANG J Q, et al. Eddy current testing of directionality in anisotropic carbon fiber reinforced polymer composite[J]. Journal of Vibration, Measurement & Diagnosis, 2012, 50(2): 700-709(in Chinese). doi: 10.16450/j.cnki.issn.1004-6801.2019.03.026
    [19] FAN W R, WANG C. A modified L1/2 regularization algorithm for electrical impedance tomography[J]. Measurement Science and Technology, 2019, 31(1): 015011.
    [20] SHI T T, ZHANG X, WANG M, et al. An adaptive non-convex hybrid total variation regularization method for image reconstruction in electrical impedance tomography[J]. Flow Measurement and Instrumentation, 2021, 79: 101937. doi: 10.1016/j.flowmeasinst.2021.101937
    [21] 寇喜鹏. 结构变分不等式与凸优化问题的若干算法研究[D]. 重庆: 重庆大学, 2015: 79-82.

    KOU X P. Study on some algorithms for structural variational inequality and convex optimization problems[D]. Chongqing: Chongqing University, 2015: 79-82(in Chinese).
    [22] LU Y, ZHONG A, LI Q. Beyond finite layer neural networks: Bridging deep architetures and numerical differential equations[C]//International Conference on Machine Learning, 2017: 3276-3285.
    [23] ZHANG X Y, CHEN X Y, WANG Z C, et al. EIT-4LDNN: A novel neural network for electrical impedance tomography[J]. Journal of Physics:Conference Series, 2021, 1757: 012013. doi: 10.1088/1742-6596/1757/1/012013
  • 加载中
图(10) / 表(1)
计量
  • 文章访问数:  278
  • HTML全文浏览量:  95
  • PDF下载量:  22
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-05-10
  • 录用日期:  2021-07-09
  • 网络出版日期:  2021-07-30
  • 整期出版日期:  2023-02-28

目录

    /

    返回文章
    返回
    常见问答