Practical Implementation of Electrical Tomography in a Distributed System to Examine the Condition of Objects

This paper presents a nondestructive method to examine the condition of objects, such as brick wall, flood embankments, landfills, air brick, and similar constructions. We used a setup made of specially built a distributed system with measuring devices to determine the moisture level of flood embankments and to test brick walls. It is an innovative solution for the evaluation buildings and tanks, both in terms of the measuring method the reconstruction algorithm. The application of modern tomographic techniques in conjunction with topological algorithms allows us to perform a noninvasive and very accurate spatial assessment of the dampness level. Electrical tomography under various names includes many techniques for tomographic imaging of the electrical parameters of an object placed in an examination area. We propose a new hybrid solution utilizing imaging techniques together with surface electrodes. Although many methods of evaluating dampness and damage exist, there is no universal solution that is optimal under a wide range of measurement conditions. Our proposed solution achieves this using the topology method for optimization. The smart tomographic measurement systems used in our solution were developed by authors belonging to the Netrix Research and Development Laboratory. These systems included measuring devices, distributed systems, algorithms, and applications for image reconstruction. Several types of reconstruction algorithms and models are explored in this paper. The solution of this optimization problem is obtained by combining the finite element method and topological algorithms. Reconstruction of 2-D examples using numerical and experimental data is shown. The proposed tomographic system consists of a central processing unit, a set of sensors (devices) and a software solution that leverages cloud computing and a big data cluster for processing, visualizing, and analyzing data (a cyber-physical system).

[1]  David S. Holder Introduction to biomedical electrical impedance tomography Electrical Impedance Tomography Methods, History and Applications ed DS Holder , 2005 .

[2]  J. Kaipio,et al.  Electrical Resistance Tomography Imaging of Concrete , 2010 .

[3]  Eric T. Chung,et al.  Electrical impedance tomography using level set representation and total variational regularization , 2005 .

[4]  Katarzyna Szulc,et al.  Topological derivative - theory and applications , 2015 .

[5]  W. Marsden I and J , 2012 .

[6]  K. Kunisch,et al.  Level-set function approach to an inverse interface problem , 2001 .

[7]  Arto Voutilainen,et al.  Three-dimensional nonstationary electrical impedance tomography with a single electrode layer , 2010 .

[8]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[9]  Jan Sokolowski,et al.  Introduction to shape optimization , 1992 .

[10]  Tomasz Rymarczyk,et al.  Using electrical impedance tomography to monitoring flood banks , 2014 .

[11]  S. Osher,et al.  Level set methods: an overview and some recent results , 2001 .

[12]  M Wang Industrial Tomography: Systems and Applications , 2015 .

[13]  T. Rymarczyk Characterization of the shape of unknown objects by inverse numerical methods , 2012 .

[14]  Jan Sokolowski,et al.  On the Topological Derivative in Shape Optimization , 1999 .

[15]  T. Rymarczyk,et al.  The Shape Reconstruction of Unknown Objects for Inverse Problems , 2012 .

[16]  S. Osher,et al.  Level Set Methods for Optimization Problems Involving Geometry and Constraints I. Frequencies of a T , 2001 .

[17]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[18]  Jerzy Hoła,et al.  Identification of moisture content in brick walls by means of impedance tomography , 2012 .

[19]  T. Johansen,et al.  State estimation and inverse problems in electrical impedance tomography: observability, convergence and regularization , 2015 .

[20]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[21]  Tomasz Rymarczyk,et al.  New methods to determine moisture areas by electrical impedance tomography , 2016 .

[22]  Lidia Jackowska-Strumiłło,et al.  Simulation of gravitational solids flow process and its parameters estimation by the use of Electrical Capacitance Tomography and Artificial Neural Networks , 2016 .

[23]  T. Rymarczyk,et al.  Measurement Methods and Image Reconstruction in Electrical Impedance Tomography , 2012 .

[24]  Arto Voutilainen,et al.  State estimation and inverse problems in electrical impedance tomography : observability , convergence and regularization , 2015 .

[25]  Robert Banasiak,et al.  Four-dimensional electrical capacitance tomography imaging using experimental data , 2009 .

[26]  Armin Lechleiter,et al.  Newton regularizations for impedance tomography: convergence by local injectivity , 2008 .

[27]  Tomasz Rymarczyk,et al.  New electrical tomographic method to determine dampness in historical buildings , 2016 .

[28]  G. Allaire,et al.  Structural optimization using topological and shape sensitivity via a level set method , 2005 .

[29]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[30]  William R B Lionheart,et al.  Uses and abuses of EIDORS: an extensible software base for EIT , 2006, Physiological measurement.

[31]  Robert Banasiak,et al.  Metrological evaluation of a 3D electrical capacitance tomography measurement system for two-phase flow fraction determination , 2013 .

[32]  Manuchehr Soleimani,et al.  A Narrow-Band Level Set Method Applied to EIT in Brain for Cryosurgery Monitoring , 2006, IEEE Transactions on Biomedical Engineering.

[33]  Waldemar T. Smolik Fast forward problem solver for image reconstruction by nonlinear optimization in electrical capacitance tomography , 2010 .