Electrical capacitance tomography (ECT) is a relatively mature non-invasive imaging technique that attempts to map dielectric permittivity of materials. ECT has become a promising monitoring technique in industrial process tomography especially in fast flow visualization. One of the most challenging tasks in further development of ECT for real applications are the computational aspects of the ECT imaging. Recently, 3D ECT has gained interest because of its potential to generate volumetric images. Computation time of image reconstruction in 3D ECT makes it more difficult for real time applications. In this paper we present a robust and computationally efficient 4D image reconstruction algorithm applied to real ECT data. The method takes advantage of the temporal correlation between 3D ECT frames to reconstruct movies of dielectric maps. Image reconstruction results are presented for the proposed algorithms for experimental ECT data of a rapidly moving object

Electrical capacitance tomography (ECT) is a promising non-invasive imaging technique that is capable of mapping the dielectric permittivity of an object. ECT has great potential to be used as a new non-destructive evaluation (NDE) method. In the majority of NDE applications for ECT there is only limited access to the targeted objects, making conventional circular array ECT impractical. In this paper, a planar array ECT system is developed and proposed for 3D subsurface imaging as a form of limited access tomography. The sensor development, practical implications, capabilities and limitations of the proposed planar array ECT system are discussed. The experimental results are used to evaluate the system performance. A depth of up to 53% of the length of the sensor array can be achieved using experimental data using an array of 4 × 3 electrodes

With the ongoing digitalization of industry, imaging sensors are becoming increasingly important for industrial process control. In addition to direct imaging techniques such as those provided by video or infrared cameras, tomographic sensors are of interest in the process industry where harsh process conditions and opaque fluids require non-intrusive and non-optical sensing techniques. Because most tomographic sensors rely on complex and often time-multiplexed excitation and measurement schemes and require computationally intensive image reconstruction, their application in the control of highly dynamic processes is often hindered. This article provides an overview of the current state of the art in fast process tomography and its potential for use in industry.