Application of Electrical Capacitance Tomography for Imaging Conductive Materials in Industrial Processes
| Autor: | Alaa Sheta, W.A. Deabes, Kheir Eddine Bouazza, Mohamed Abdelrahman |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Article Subject
Artificial neural network Computer science 020208 electrical & electronic engineering Process (computing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Fuzzy control system Electrical capacitance tomography Solver Inverse problem Capacitance Control and Systems Engineering lcsh:Technology (General) 0202 electrical engineering electronic engineering information engineering Electronic engineering lcsh:T1-995 020201 artificial intelligence & image processing Tomography Electrical and Electronic Engineering Instrumentation |
| Zdroj: | Journal of Sensors, Vol 2019 (2019) |
| ISSN: | 1687-7268 |
| Popis: | This paper presents highly robust, novel approaches to solving the forward and inverse problems of an Electrical Capacitance Tomography (ECT) system for imaging conductive materials. ECT is one of the standard tomography techniques for industrial imaging. An ECT technique is nonintrusive and rapid and requires a low burden cost. However, the ECT system still suffers from a soft-field problem which adversely affects the quality of the reconstructed images. Although many image reconstruction algorithms have been developed, still the generated images are inaccurate and poor. In this work, the Capacitance Artificial Neural Network (CANN) system is presented as a solver for the forward problem to calculate the estimated capacitance measurements. Moreover, the Metal Filled Fuzzy System (MFFS) is proposed as a solver for the inverse problem to construct the metal images. To assess the proposed approaches, we conducted extensive experiments on image metal distributions in the lost foam casting (LFC) process to light the reliability of the system and its efficiency. The experimental results showed that the system is sensible and superior. |
| Databáze: | OpenAIRE |
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