User:Rambali
Electrical Capacitance Tomography (ECT)[edit]
 | I, the creator of this work, hereby grant the permission to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. Subject to disclaimers. |
   | If this file is eligible for relicensing, it may also be used under the Creative Commons Attribution-ShareAlike 3.0 license. The relicensing status of this image has not yet been reviewed. You can help. |  |
ECT is a tomographic technique which is popular especially for industrial applications. The term tomography was coined from the Greek words tomos meaning slice and graphos meaning image. ECT measurements are used to determine the permittivity distribution of materials inside pipes/cylindrical vessels by obtaining capacitance measurements from various combinations of plate electrodes. ECT is a soft-field technique, implying the sensing field (electric potential) is spread over the entire volume and the path is ill-defined. The advantages of ECT are that it’s a fast, non-invasive, non-intrusive technique and is robust under hostile conditions. ECT produces good frame rate of images up to 200 fps. Images reconstructed using ECT have a low resolution, of around 3-10% of the vessel diameter. However this resolution is sufficient for industrial applications. In an ECT system there are M independent capacitance measurements, where
M = N (N-1)/ 2
Where N is the number of electrodes of the ECT sensor. For example for an 8 electrode sensor there are 28 independent measurements. The measurement frequency is usually in the order of 1 MHz. When capacitance measurements are to be taken, one electrode of the ECT sensor is activated while all the other electrodes are at ground potential. This is repeated sequentially until all the electrodes have been activated once and the remaining electrodes have all been detector electrodes. The measurements can be processed using suitable image reconstruction algorithms to obtain pixel-based images of material distribution within the sensor.