High Resolution Electrical Resistance Tomography Image Reconstruction And Characterization Study Of The Effects Of Three Dimensional Electric Field

In recent years, Electrical Resistance Tomography technology (ERT) has been developing rapidly and now has come to the stage of industrial applications. It reconstructs the internal conductivity(or impedance) profile of an object from boundary measurements of voltages and the corresponding current fluxes. ERT has several advantages over other traditional process-parameter-measurement techniques, such as its non-invasion, fast-responding, low cost, non-radiation, flexible measurement options etc. So it is playing an important role in the industrial process.The main content given in this paper covers two important aspects in ERT research aimed at increasing the requirements of resolution image reconstruction. One work relates to image reconstruction. By incorporating the existed Linear Back Projection (LBP) method, the Modified Newton Raphson (MNR) method, the Sentiviey Theory Method(STM)with a Genetic Algorithm(GA), a Bayesian based combinatorial ERT image reconstruction algorithm is developed. The performance of such a combinatorial is detail evaluated with respect to the enhancement it made on image correlation coefficient and image variance. The impact of measurement noise on reconstruction is also discussed, through which the degree of reconstruction robustness can be observed. The result of this work demonstrates that it is possible to enhance the reconstructed image resolution using the combinatorial method described in this paper. The second work relates to a 2D modeling compensation with 3D modeling results. The work is significant when a 2D ERT model is used to perform an image reconstruction while the actual data is obtained from a background where 3D influence of medium is not insignificant. Several different ERT sensor structures are presented in the paper to observe the 3D influence of electrode size, electrode separation, guard electrodes etc on electrical potential distribution. Data are then compensated to fit a 2D ERT model and the comparisons of reconstructed images obtained with or without data compensation are made. It is great significance in understanding and mastering sensor performance as well as the accurate measurement of the object detected. Finally a simulation software package combining ANSYS and MATLAB for the above mentioned work is given.