Electrical capacitance tomography: a review on portable ECT system and hardware design

Rashid, Wan Norhisyam Abd; Mohamad, Elmy Johana; Rahim, Ruzairi Abdul; Abdullah, J. B.; Ameran, Hanis Liyana Mohmad

doi:10.1108/sr-06-2015-0089

Cited by 23 publications

(10 citation statements)

References 19 publications

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“…The flask is earthed to eradicate external noises and stray capacitance effects [56]. Grounded guards are uniformly placed between the electrodes to increase the sensors' dynamic range and keep the electrical field uniform.…”

Section: Ect System For Conductive Materialsmentioning

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: Ect System For Conductive Materialsmentioning

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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“…Assume we have a group of n electrodes, thus, each two formulate a capacitive sensor. There is an external shield 3 Journal of Sensors (flask) around these electrodes to eliminate external noises and stray capacitance effects [27], as shown in Figure 4. The mutual capacitance is measured between every two electrodes where one active electrode works as a transmitter and the other grounded electrodes work as receivers.…”

Section: Ect Systemmentioning

confidence: 99%

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

et al. 2019

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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.

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“…Linear back projection (LBP) technique is the real-time reconstruction of process image with its fast computational speed. Its simplicity makes up as the most preferable technique used especially in process tomography [4]. However, due to its simplicity, several draw back such as blurry image issues and inaccuracy has been observed [7].…”

Section: Linear Back Projectionmentioning

confidence: 99%

Image Reconstruction Enhancement Using Convolution Back Projection Method for Electrical Tomography Application

Pusppanathan

Mansor

Rahim

et al. 2018

IJIE

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Electrical capacitance tomography: a review on portable ECT system and hardware design

Cited by 23 publications

References 19 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

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

Image Reconstruction Enhancement Using Convolution Back Projection Method for Electrical Tomography Application

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