Application of Electrical Capacitance Tomography for Imaging Conductive Materials in Industrial Processes

Deabes, Wael; Sheta, Alaa; Bouazza, Kheir Eddine; Abdelrahman, Mohamed

doi:10.1155/2019/4208349

Cited by 15 publications

(8 citation statements)

References 41 publications

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“…proposed Capacitance Artificial Neural Network (CANN) system in [24] as a solver for the forward problem and Metal Filled Fuzzy System (MFFS) to solve the inverse problem to construct the metal distribution images. Li et al tried to generate ECT images using the Backpropagation (BP) and Radial Basis Function (RBF) neural networks [25].…”

Section: A Literature Reviewmentioning

confidence: 99%

ECT-LSTM-RNN: An Electrical Capacitance Tomography Model-Based Long Short-Term Memory Recurrent Neural Networks for Conductive Materials

2021

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Image reconstruction for industrial applications based on Electrical Capacitance Tomography (ECT) has been broadly applied. The goal of image reconstruction based ECT is to locate the distribution of permittivity for the dielectric substances along the cross-section based on the collected capacitance data. In the ECT-based image reconstruction process: (1) the relationship between capacitance measurements and permittivity distribution is nonlinear, (2) the capacitance measurements collected during image reconstruction are inadequate due to the limited number of electrodes, and (3) the reconstruction process is subject to noise leading to an ill-posed problem. Thence, constructing an accurate algorithm for real images is critical to overcoming such restrictions. This paper presents novel image reconstruction methods using Deep Learning for solving the forward and inverse problems of the ECT system for generating high-quality images of conductive materials in the Lost Foam Casting (LFC) process. Here, Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models were implemented to predict the distribution of metal filling for the LFC process-based ECT. The recurrent connection and the gating mechanism of the LSTM is capable of extracting the contextual information that is repeatedly passing through the neural network while filtering out the noise caused by adverse factors. Experimental results showed that the presented ECT-LSTM-RNN model is highly reliable for industrial applications and can be utilized for other manufacturing processes.

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Section: A Literature Reviewmentioning

confidence: 99%

ECT-LSTM-RNN: An Electrical Capacitance Tomography Model-Based Long Short-Term Memory Recurrent Neural Networks for Conductive Materials

2021

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“…Researchers from the ECT area tried to use machine learning-based approaches to solve the problem of image reconstruction. Deabes et al solved the forward problem using NN system, and they proposed a Multi-Fuzzy System (MFS) to generate images of conductive materials in a Lost Foam Casting (LFC) process [20,21].…”

Section: Introductionmentioning

confidence: 99%

Residual Autoencoder Deep Neural Network for Electrical Capacitance燭omography

Deabes¹,

Bouazza²

2022

Computers, Materials &Amp; Continua

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Great achievements have been made during the last decades in the field of Electrical Capacitance Tomography (ECT) image reconstruction. However, there is still a need to make these image reconstruction results faster and of better quality. Recently, Deep Learning (DL) is flourishing and is adopted in many fields. The DL is very good at dealing with complex nonlinear functions and it is built using several series of Artificial Neural Networks (ANNs). An ECT image reconstruction model using DNN is proposed in this paper. The proposed model mainly uses Residual Autoencoder called (ECT_ResAE). A large-scale dataset of 320 k instances have been generated to train and test the proposed ECT_ResAE model. Each instance contains two vectors; a distinct permittivity distribution and its corresponding capacitance measurements. The capacitance vector has been modulated to generate a 66×66 image, and represented to the ECT_ResAE as an input. The scalability and practicability of the ECT_ResAE network are tested using noisy data, new samples, and experimental data. The experimental results show that the proposed ECT_ResAE image reconstruction model provides accurate reconstructed images. It achieved an average image Correlation Coefficient (CC) of more than 99% and an average Relative Image Error (IE) around 8.5%.

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“…1 shows these main components of the ECT system [25]. The electrical tomography process usually starts by collecting the data from the sensors, reconstructs the distribution images, and displays the results [26].…”

Section: Introductionmentioning

confidence: 99%

Efficient Image Reconstruction Algorithm for ECT System Using Local Ensemble Transform Kalman Filter

Deabes

Bouazza

2021

IEEE Access

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One of the vital processes that should be monitored and analyzed continuously in the oilgas and petroleum-related industries is the multi-phase flow inside pipes. Multi-phase flow means flowing two or more phases of gas, liquid, or solid inside a pipe. Electrical Capacitance Tomography (ECT) is a feasible and economical solution for monitoring dynamic applications. The ECT system offers the benefits of no radiation, non-intrusive, and non-invasive. Despite its potential, ECT systems deployment's major limitation is the crucial need to develop rapid image reconstruction algorithms. In this paper, a Local Ensemble Transform Kalman Filter (LETKF) is developed as a non-linear system estimator for reconstructing images in the ECT system. This method manages each node of the model independently by assimilating only the observations at a predefined distance. The localized approach of the LETKF gives it high computational efficiency allowing it to be applied to large dynamic systems. A quantitative analysis using Image Error (IE) and Coefficient Correlation (CC) measures has been applied to prove the effectiveness of the proposed algorithm. Indeed, the IE has been significantly decreased (around 62%), and the CC greatly increased (around 58%). Then, the influence of the noise was discussed. The results are promising and prove the algorithm feasibility.INDEX TERMS ECT, image reconstruction, Kalman filter, multi-phase flow.

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Application of Electrical Capacitance Tomography for Imaging Conductive Materials in Industrial Processes

Cited by 15 publications

References 41 publications

ECT-LSTM-RNN: An Electrical Capacitance Tomography Model-Based Long Short-Term Memory Recurrent Neural Networks for Conductive Materials

ECT-LSTM-RNN: An Electrical Capacitance Tomography Model-Based Long Short-Term Memory Recurrent Neural Networks for Conductive Materials

Residual Autoencoder Deep Neural Network for Electrical Capacitance燭omography

Efficient Image Reconstruction Algorithm for ECT System Using Local Ensemble Transform Kalman Filter

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