Sensitivity matrix construction for electrical capacitance tomography based on the difference model

Guo, Zhiheng; Shao, Fuqun; Lv, Decai

doi:10.1016/j.flowmeasinst.2008.11.003

Cited by 24 publications

(9 citation statements)

References 9 publications

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“…Moreover, the desirable reconstructed images tend to incorporate more smoothness constrains in high-sensitivity regions, together with highly oscillatory sensitivity than ones in low-sensitivity regions. The local element sensitivity gradient magnitudes represent the variation of the sensitivity, which has been used to generate a new sensitivity normalization model [12]. In this work, the sensitivity central differences are used to quantify the spacevariant regularization levels to achieve non-uniform smoothness constrains.…”

Section: Non-uniform Regularization Methodsmentioning

confidence: 99%

A non-uniform regularization image reconstruction algorithm for electrical capacitance tomography

Guo

2011

2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)

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An non-uniform regularization algorithm for electrical capacitance tomography is presented, and the regularization matrix of the new algorithm is constructed using a combination of an identity matrix and inhomogeneous weighted Laplacian operator related to the gradient magnitude of the sensitivity. Simulation experimentations have been performed to verify that the new regularization algorithm is capable of reconstructing superior image quality over the standard Tikhonov regularization.

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Section: Non-uniform Regularization Methodsmentioning

confidence: 99%

A non-uniform regularization image reconstruction algorithm for electrical capacitance tomography

Guo

2011

2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)

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“…The sensitivity matrix and normalization capacitances are needed for all the algorithms of both classes. The commonnly used normalization capacitance model is parallel normalization model, and another approach considers series normalization model [3]. Recently, the normalization capacitance model based on the distance between electrical field centre lines (EFCL) of each electrode pairs and perturbed element was presented [4].…”

Section: Introductionmentioning

confidence: 99%

A Mixed Normalization Capacitance Model for Electrical Capacitance Tomography

Zhang¹,

Zhu²,

Zhang³

2016

Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)

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Abstract. Electrical capacitance tomography (ECT) is a technique developed in recent 30 years, which aims to measure multi-phase flow. The image reconstruction for ECT is an inverse problem to find the permittivity distribution in a pipe. The normalized capacitance measurements are needed during the process of image reconstruction. Parallel normalization model is the most commonly used normalization model, and series normalization model was also studied by some researchers. In this paper, a mixed normalization model based on electrical field center line (EFCL) and multi-threshold method was presented. The sensitivity matrices based on different normalization models were obtained and image reconstruction was carried out accordingly. Simulation and static experiment indicate that reconstructed images with higher quality can be obtained based on the presented model.

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“…That is to say, there are always numerous particles passing a cross-section simultaneously, thus forming an obvious profile of the solid-phase, which can be imaged by tomography-based methods [ 5 , 11 , 14 ]. ESAs with arc-shaped sensor units usually adopt array signal processing algorithms based on electrical capacitance tomography [ 11 , 12 , 16 , 22 , 23 ]. They are suitable for dense gas-solid flows.…”

Section: Introductionmentioning

confidence: 99%

“…That is to say, there are only one or just a few particles passing a cross-section simultaneously. One representative application is exhaust debris monitoring-based PHM of gas turbines [ 7 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. In this case, the occurrences of fault-related particles are accidental and instantaneous.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Dynamic Sensitivity of Hemisphere-Shaped Electrostatic Sensors’ Circular Array for Charged Particle Monitoring

Tang

Chen

et al. 2016

Sensors

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Electrostatic sensor arrays (ESAs) are promising in industrial applications related to charged particle monitoring. Sensitivity is a fundamental and commonly-used sensing characteristic of an ESA. However, the usually used spatial sensitivity, which is called static sensitivity here, is not proper for moving particles or capable of reflecting array signal processing algorithms integrated in an ESA. Besides, reports on ESAs for intermittent particles are scarce yet, especially lacking suitable array signal processing algorithms. To solve the problems, the dynamic sensitivity of ESA is proposed, and a hemisphere-shaped electrostatic sensors’ circular array (HSESCA) along with its application in intermittent particle monitoring are taken as an example. In detail, a sensing model of the HSESCA is built. On this basis, its array signals are analyzed; the dynamic sensitivity is thereupon defined by analyzing the processing of the array signals. Besides, a component extraction-based array signal processing algorithm for intermittent particles is proposed, and the corresponding dynamic sensitivity is analyzed quantitatively. Moreover, simulated and experimental results are discussed, which validate the accuracy of the models and the effectiveness of the relevant approaches. The proposed dynamic sensitivity of ESA, as well as the array signal processing algorithm are expected to provide references in modeling, designing and using ESAs.

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Sensitivity matrix construction for electrical capacitance tomography based on the difference model

Cited by 24 publications

References 9 publications

A non-uniform regularization image reconstruction algorithm for electrical capacitance tomography

A non-uniform regularization image reconstruction algorithm for electrical capacitance tomography

A Mixed Normalization Capacitance Model for Electrical Capacitance Tomography

Analysis of the Dynamic Sensitivity of Hemisphere-Shaped Electrostatic Sensors’ Circular Array for Charged Particle Monitoring

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