基于改进联合稀疏EIT算法的CFRP材料检测

马敏; 于洁; 范文茹

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

中国民航大学电子信息与自动化学院，天津 300300

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
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- 文章访问数: 230
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- PDF下载量: 21
- 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

CFRP material detection based on improved joint sparse EIT algorithm

College of Electronic Information and Automation，Civil Aviation University of China，Tianjin 300300，China

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

More Information

Corresponding author: E-mail：mm5739@163.com

摘要

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L₁和L₂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。
- 电阻抗层析成像技术 /
- 碳纤维增强复合材料 /
- 损伤检测 /
- 图像重建 /
- 联合稀疏
Abstract:
Aiming at the highly ill-posed problem of the application of electrical impedance tomography (EIT) to the damage detection of carbon fiber reinforced polymer (CFRP), a joint ${L_1}$ and ${L_2}$ norm sparse regularization functional model was proposed, and a new constraint is constructed to optimize the solution during the iterative process. The simulation results show that, compared with other traditional algorithms, the improved joint sparse EIT algorithm can effectively improve the electrode artifacts of the damage image, improve the clarity of damage edge, and enhance the accuracy of damage identification and location. The experimental results of CFRP laminate detection show that the improved joint sparse EIT algorithm can improve the anti-interference ability of the image reconstruction, and has good robustness and applicability.
- electrical impedance tomography /
- carbon fiber reinforced polymer /
- damage detection /
- image reconstruction /
- joint sparse

HTML全文

图 1 CFRP模型

Figure 1. Model of CFRP

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图 2 EIT系统组成

Figure 2. EIT system composition

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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

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图 4 图像重建结果对比

Figure 4. Comparison of image reconstruction results

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图 5 相关系数对比

Figure 5. Comparison of correlation coefficient

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图 6 面积误差对比

Figure 6. Comparison of area error

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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

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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

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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

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图 10 CFRP 损伤检测成像效果对比

Figure 10. Comparison of imaging results of CFRP damage detection

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表 1 CFRP损伤模型

Table 1. CFRP damage model

模型 1 2 3 4 5

图示

下载: 导出CSV

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