NONDESTRUCTIVE TESTING METHOD FOR ELECTRICAL CAPACITANCE TOMOGRAPHY BASED ON IMAGE RECONSTRUCTION OF ROTATING ELECTRODES

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

The electrical capacitance tomography (ECT) is a visual nondestructive testing technology. The relative positional distribution between the electrodes and the phantom object affects the accuracy of the reconstructed image. To solve this problem, an image reconstruction method and image fusion algorithm of ECT system based on rotating electrodes are proposed. First, 4 image reconstruction algorithms are employed to reconstruct the experimental model, the Landweber iterative algorithm based on Tikhonov regularization presents the best performance. Then, by rotation the 12 electrodes 4 times, we can obtain 5 sets of capacitance data, and obtain 5 images. Finally, the fusion results can be obtained by performing the adaptive weighted fusion on these 5 images. Results show that the adaptive weighted image fusion method based on rotation electrodes improves the quality of reconstructed images and effectively reduces the artefacts.

Авторлар туралы

Zhang Qian

Guangxi University of Science and Technology

Email: QianZhang283370482@163.com
Liuzhou, China

Mo Hong

Kunming University of Science and Technology

Kunming, China

Li Ruxue

Guangxi University of Science and Technology; Guangxi Key Laboratory of Multidimensional Information Fusion for Intelligent Vehicles

Liuzhou, China

Liang Chenghua

Guangxi University of Science and Technology; Guangxi Key Laboratory of Multidimensional Information Fusion for Intelligent Vehicles

Email: chenghua.liang@gxust.edu.cn
Liuzhou, China

Luo Junhua

Tomsk Polytechnic University

Tomsk, Russia

Әдебиет тізімі

  1. Ismail I., Gamio J., Bukhari S.A., Yang W. Tomography for multi-phase flow measurement in the oil industry // Flow measurement and instrumentation. 2005. V. 16. P. 145—155.
  2. Hosseini M., Kaasinen A., Aliyari Shoorehdeli M., Link G., Lähivaara T., Vauhkonen M. System identification of conveyor belt microwave drying process of polymer foams using electrical capacitance tomography // Sensors. 2021. V. 21. P. 7170.
  3. Gunes C., Chowdhury S., Marashdeh Q.M., Teixeira F.L. Displacement-current phase tomography and electrical capacitance tomography for air-water flow systems / In Proceedings of the 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS). IEEE. 2017. P. 1—3.
  4. Rimpiläinen V., Poutiainen S., Heikkinen L.M., Savolainen T., Vauhkonen M., Ketolainen J. Electrical capacitance tomography as a monitoring tool for high-shear mixing and granulation // Chemical Engineering Science. 2011. V. 66. P. 4090—4100.
  5. Wang A., Marashdeh Q., Motil B.J., Fan L.S. Electrical capacitance volume tomography for imaging of pulsating flows in a trickle bed // Chemical Engineering Science. 2014. V. 119. P. 77—87.
  6. Rasel R.K., Zuccarelli C.E., Marashdeh Q.M., Fan L.S., Teixeira F.L. Toward multiphase flow decomposition based on electrical capacitance tomography sensors // IEEE Sensors Journal. 2017. V. 17. P. 8027—8036.
  7. Rymarczyk T., Kłosowski G., Kozłowski E. A non-destructive system based on electrical tomography and machine learning to analyze the moisture of buildings // Sensors. 2018. V. 18. P. 2285.
  8. Zhang L., Dai L. Image reconstruction of electrical capacitance tomography based on an efficient sparse Bayesian learning algorithm // IEEE Transactions on Instrumentation and Measurement. 2022. V. 71. P. 1—14.
  9. Lei J., Liu S., Wang X., Liu Q. An image reconstruction algorithm for electrical capacitance tomography based on robust principle component analysis // Sensors. 2013. V. 13. P. 2076—2092.
  10. Deabes W., Abdel-Hakim A.E., Bouazza K.E., Althobaiti H. Adversarial resolution enhancement for electrical capacitance tomography image reconstruction // Sensors. 2022. V. 22. P. 3142.
  11. Niderla K., Rymarczyk T., Sikora J. Manufacturing planning and control system using tomographic sensors // Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie ´Srodowiska. 2018. V. 8. P. 29—34.
  12. Peng L., Ye J., Lu G., Yang W. Evaluation of effect of number of electrodes in ECT sensors on image quality // IEEE Sensors Journal. 2011. V. 12. P. 1554—1565./
  13. Huang C.N., Yu F.M., Chung H.Y. Rotational electrical impedance tomography // Measurement Science and Technology. 2007. V. 18. P. 2958.
  14. Frounchi J., Bazzazi A. High resolution rotary capacitance tomography system / In Proceedings of the Proc. 3rd World Congress on IPT (Banff, Canada). 2003. P. 858—63.
  15. Murphy S.C., York T.A. Electrical impedance tomography with non-stationary electrodes // Measurement Science and Technology. 2006. V. 17. P. 3042.
  16. Liu Z., Babout L., Banasiak R., Sankowski D. Effectiveness of rotatable sensor to improve image accuracy of ECT system // Flow Measurement and Instrumentation. 2010. V. 21. P. 219—227.
  17. Wajman R., Banasiak R., Babout L. On the use of a rotatable ect sensor to investigate dense phase flow: A feasibility study // Sensors. 2020. V. 20. P. 4854.
  18. Guo Z. New normalization method of imaging data for electrical capacitance tomography / In Proceedings of the 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC). IEEE. 2011. P. 1126—1130.
  19. Mou C.H., Peng L.H., Yao D.Y., Zhang B.F., Xiao D.Y. A calculation method of sensitivity distribution with electrical capacitance tomography // Chinese Journal of Computational Physics. 2006. V. 23. P. 87.
  20. Jing L., Liu S., Zhihong L. et al. An image reconstruction algorithm based on the extended Tikhonov regularization method for electrical capacitance tomography // Measurement. 2009. V. 42. P. 368—376.
  21. Qi-Nian J., Zong-Yi H. On an a posteriori parameter choice strategy for Tikhonov regularization of nonlinear ill-posed problems // Numerische Mathematik. 1999. V. 83. P. 139—159.
  22. Yang W., Spink D., York T., McCann H. An image-reconstruction algorithm based on Landweber’s iteration method for electrical-capacitance tomography // Measurement Science and Technology. 1999. V. 10. P. 1065.
  23. Yang W., Peng L. Image reconstruction algorithms for electrical capacitance tomography // Measurement Science and Technology. 2002. V. 14. P. R1.
  24. Xie M., Yang H., Zhao K., Yuan B., Li J., Liu J., Ying J., Huang Y., Ou J. Regularization parameter optimization based on the constraint of Landweber algorithm for electrical capacitance tomography // Flow Measurement and Instrumentation. 2019. V. 69. P. 101620.

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