Capacitance Imaging System Research Articles

Split Bregman algorithm based on adaptive parameter for planar array sensor imaging

Planar array capacitance imaging is a visualization imaging technique based on the edge effect of electric fields. However, there are ill-posed problem and soft-field effects in the imaging process, which results in unstable and susceptible imaging. To address this issue, the inverse solution of planar array capacitive imaging system is transformed into an optimization problem with the approximate solution of linear-squares. A new cost function with total variation sparse reconstruction model is presented, which considers the sparsity of regularization operator to improve the stability of the inversion process. The adaptive split Bregman iterative algorithm is proposed to solve the inverse optimization problem. The computation is simplified by splitting the complex optimization problem into simple subproblems. The results show that the algorithm can obtain stable model updates, and numerical experiments prove the effectiveness and reliability of the algorithm.

Planar array capacitive imaging method based on data optimization

This paper studies the problem of unstable capacitance data collected by the planar array capacitance imaging system, and proposes a method of capacitance data optimization based on improved fuzzy c-means clustering (FCM) algorithm. For the sensor with 3 × 4 array electrodes, considering that the coplanar arrangement of electrodes produces a weak fringe electric field, the measured capacitance data will be unstable. This method first uses the optimal solution of the particle swarm optimization algorithm as the initial clustering center of the FCM algorithm. Then the improved FCM algorithm is used to optimize the unstable capacitance data. This improved method can avoid falling into local optimization and makes the capacitance data closer to the real values. The optimized capacitance data are used to reconstruction image by Linear Back Projection (LBP) algorithm and Landweber algorithm, respectively. The experimental results show that the stability of the processed capacitance data is enhanced, the relative image error I e is reduced, and the image correlation coefficient I c is improved. It can be seen that the effectiveness of the proposed method for defects detection has been validated. • In this paper, the quality of imaging is improved by reducing the noise of capacitance data measured by planar array capacitance imaging system. • In this paper, the FCM algorithm optimized by PSO algorithm is applied to planar array capacitance imaging for the first time.

Planar array capacitance imaging based on adaptive Kalman filter

Planar array capacitance imaging system has the characteristics of uneven distribution of sensitive field, serious ill posed problem and measurement data vulnerable to external interference, and these characteristics will make the image artifacts particularly serious, affect the quality of the reconstructed image, and even determine the number of defects with difficulty. In order to solve the problem that the edge electric field and ill conditioned characteristics of planar array electrode seriously affect the quality of capacitance image reconstruction, an improved image reconstruction algorithm based on adaptive Kalman filter is proposed to reduce the noise of capacitance data and dielectric constant matrix. On the basis of constructing the state model of planar array capacitance imaging with noise, the maximum likelihood criterion is used to estimate and modify the noise variance matrix of dielectric constant matrix on-line, and the noise variance matrix of dielectric constant matrix is modified in real time. In order to restrain the filtering divergence and accelerate the convergence speed, different weighting coefficients are provided for the error covariance matrix with time going by. Through designing four kinds of samples from simple to complex structure, the defect detection experiment of composite structure is carried out. The experimental results show that compared with linear back projection (LBP), Tikhonov regularization (TR) algorithm and Kalman filtering algorithm, the image error of adaptive Kalman filtering algorithm can be reduced by about 20%, the image correlation coefficient is as high as 0.79 and the convergence speed can be improved by about 15%, the image artifacts of the four samples are greatly reduced. The experimental data show that the proposed adaptive Kalman filter image reconstruction algorithm can effectively reduce the noise of capacitance and permittivity matrix, enhance the stability of planar array capacitance imaging, and reduce the image error, so that the quality of the image can be significantly improved. This study provides a strong technical basis for improving the quantization accuracy of planar array capacitance imaging detection. In the future, we will further consider the image reconstruction under the condition of complex object field.

Planar capacitive imaging for composite delamination damage characterization

The objective of this study is to develop and validate a noncontact, nondestructive inspection and imaging method for rapidly assessing the subsurface condition of carbon fiber-reinforced polymer (CFRP) composite structures. The method works on the principle of planar electrical capacitance tomography (ECT). Unlike conventional ECT systems, electrodes are arranged on a rectangular planar surface in the form of a grid. The volume on one side of the electrode plane is interrogated with an electric field, and the mutual capacitances between the electrodes are measured. The volumetric electrical permittivity distribution of the interrogated region is then reconstructed from the measured capacitance responses. In this work, an ECT image reconstruction algorithm was implemented, and a customized planar capacitive imaging system was prototyped. First, different objects were placed near the electrode array, and the corresponding volumetric change in electrical permittivity was successfully captured. Second, the planar ECT system was employed for detecting artificially introduced subsurface defects in 3D-printed objects. Third, CFRP specimens with different sizes of single-layer delamination were fabricated and subjected to ECT interrogation. The results confirmed that the planar ECT system could detect the location and size of delamination in CFRP panels. The accuracy and resolution of the planar ECT prototype were also characterized.

An imaging algorithm of planar array capacitance sensor for defect detection

Planar array capacitance imaging is a feasible solution for detecting defects in composite components. However, the coplanar arrangement of electrodes in the imaging system renders a soft field effect, which results in unstable or susceptible imaging. The inverse problem of a planar array capacitance sensor system is ill-posed. Hence, a wavelet fusion combined multi-objective threshold programming imaging algorithm is proposed for a planar array capacitive imaging system. Furthermore, initial values of conjugate gradient (CG) and Newton–Raphson (NR) algorithms are optimized using the Tikhonov regularization algorithm. Subsequently, wavelet fusion is introduced to fuse images obtained using the CG and NR algorithms to acquire more detailed information. To further improve the reconstructed quality, a multi-objective threshold programming strategy is proposed. The final reconstruction image is obtained using the optimal threshold. Experimental results show a significantly improved quality of the reconstructed image, thereby verifying the effectiveness of the presented algorithm.

Multichannel Capacitive Imaging of Gas Vortex in Swirling Two-Phase Flows Using Parametric Reconstruction

Swirl-based inline phase separation is a promising approach in the process industry with potential application in oil and gas separation in petroleum industry. To increase the efficiency of the separation, the process may be controlled. In this direction, the position and the diameter of the gas vortex are two parameters that can be used in the control loop, provided that they can be non-intrusively estimated. This article presents a capacitive sensor-based imaging method to extract these geometrical parameters. The proposed method consists in obtaining high-temporal resolution capacitance measurements at the pipe boundary of a test rig, which in turn are used in a reconstruction and extracting the targeted parameters of the gas vortex. The calculated parameters are then used to visually present the swirling flow. The measurement system was evaluated quantitatively by performing experiments with different phantoms of known diameters and positions in the sensing area. Dynamic measurements were also performed in a test rig for liquid-gas swirling flow. The capacitive imaging system is capable of detecting characteristics of the flow for a wide range of gas and liquid flow rates. A qualitative analysis was also carried out by comparing time series of the capacitive images with high-speed camera recording. The geometrical parameters obtained by the proposed approach presents a good agreement with the real data, with a root mean square deviation of 0.76mm for diameter and 0.88mm for vortex position. It can be utilized in future work as an alternative or complementary input for the controlled inline liquid-gas separation system.

Image reconstruction for field-focusing capacitance imaging

For online monitoring of multi-phase flows of non-conductive materials, a field-focusing capacitance imaging system has been developed. With a field focusing capacitance sensor, a tomographic image based on capacitance measurements is used directly to map the material distribution. However, it is difficult to achieve a required accuracy for measurement of void fraction from the capacitance image, because of the soft-field effect of the capacitance sensor. In this paper, the forward and inverse problems with a field-focusing capacitance sensor are described. Simulation and experimental results show that deconvolution-based algorithms can reduce the blurring artefact and can reconstruct an image close to the original distribution.

Capacitive system for three-dimensional imaging of fluidized-bed density

The U.S. Department of Energy's Morgantown Energy Technology Center has been developing a capacitance imaging system (CIS) to support its fluidized-bed research programs. A second-generation system for capacitively imaging a cold, laboratory-scale, 15.24-cm diameter fluidized bed is described. The CIS acquires interelectrode, bed-crossing displacement current data to provide 193-pixel density values at four 2.54-cm vertically spaced levels and presents a three-dimensional density display at a rate of 30 maps per second in real time. The CIS also stores displacement current data at a rate of 60 density maps per level per second for post-run analysis. Different means of data processing are described that produce one method for real-time display and two methods for post-run analysis of data. The results of calibration and fluidization tests are presented, together with the errors associated with each of the methods for the known pixel densities in the calibration tests. Improvement in the calibration procedure to reduce these errors is proposed. Test results indicate the CIS would be a useful tool for research and monitoring operations in two-phase systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A new reconstruction algorithm for process tomography

In this work a new reconstruction algorithm for use with oil/gas pipe flow imaging has been developed. Accurate images of representative oil/gas distributions (that is, flow regimes) occurring in flow pipes has been obtained. A capacitance sensor system has been used. In the algorithm the oil/gas distribution has been represented by a set of parameters describing the oil/gas interface. A finite-element-based mathematical simulator of the multi-electrode capacitance sensor system has been developed. The simulator is capable of calculating the capacitances for a set of input parameters (namely for a given oil/gas distribution). The reconstruction algorithm calculates the image by finding the parameters that give fewest discrepancies between calculated and measured capacitances, using an optimization routine. All regimes tested in this work have been successfully reconstructed with the new algorithm, and the reconstructions are compared with images produced by the well-known linear back projection algorithm. The new algorithm has been tested using data from a capacitance imaging system; however, it can in principle be used with other imaging techniques.

A capacitive system for three-dimensional imaging of fluidized beds

A novel capacitance imaging system (CIS) has been developed which has unique capabilities for providing real-time, three-dimensional monitoring of fluidized-bed density. While the system was developed primarily to serve as a high-speed, density-imaging tool in fluidization research and development, it could also be used in other applications, such as pipeline transfer of solids, where multiphase dielectric material density changes are to be monitored. The CIS uses 16 evenly spaced, circumferentially located electrodes at each of four levels evenly spaced along the axis of a 6-in. diam fluidized bed as the density-sensing elements. The electronic system utilizes interelectrode displacement current values to obtain bed density estimates at each of the 49 pixels defined at each of the four levels. The system provides a real-time display of each of the four density maps at a rate of 30 maps per second at each level. The system also stores displacement current data, updated 100 times per second, for post-run analysis of bed behavior. The CIS capabilities are demonstrated for a plastic bead bed material. Density maps show the progression of a low-density region upward through a fluidized bed. Experimental use of the system by others produced values of bubble dimension and velocity that are consistent with values predicted by accepted theory.