Capacitive Imaging Research Articles

Lightning Damage Evaluation in Carbon Fiber Composite Polymer Structure

While metallic aircraft structures are largely unaffected by lightning strikes, not the same statement could be made about carbon fibre reinforced polymer (CFRP) ones. CFRPs have low electrical conductivity and high anisotropy, and for this reason they are covered with metallic meshes, generally of aluminium or copper, to redistribute the electrical charge when the plane encounters lightning events. During these phenomena, electrical discharges could lead to physical damage, called direct effects, such as: ablation, erosion, explosion, structural weakening and degradation. Although relatively rare, when aircraft is affected by lightning strikes, structural inspections are necessary to determine the need and magnitude of repairs. Since the extent of lighting strike damage on laminated composite is difficult to determine and quantify visually, other non-destructive evaluation (NDE) techniques must be used. In this work, we complement established techniques, such as pulse-echo ultrasonics, X-ray radiography, eddy currents, and thermography with a novel investigative approach, based on planar capacitive sensing and imaging. This has the advantage of being non-contact, and requiring only single-side access to the part. The technique’s capabilities to detect dielectric changes and breakages in conductive layers make it an ideal candidate for the investigation of CFRP aircraft structure for lightning effects. The outcomes of capacitive imaging inspections on laboratory introduced lightning damage on CFRP panels are discussed along the advantages and disadvantages of the technique compared to more established ones.

Differentiating three distinct kinds of flaws in oil and gas pipelines derived from the spacing effect of capacitive imaging sensors

Purpose This paper aims to apply the spacing effect of capacitive imaging (CI) sensors to inspect and differentiate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module in oil and gas pipelines simultaneously. Through experimental verification, a method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors has been demonstrated. Design/methodology/approach A 3Dimensions (3D) model for simulating the inspection of these flaws was established by using COMSOL. A novel CI sensor with adjustable working electrode spacing was designed, and a modular CI system was developed to substantiate the theoretical findings with experimental evidence. A method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors was established. Findings The results indicate that the method can successfully discriminate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module using CI sensors. Originality/value The method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors is vital for keeping the transportation safety of oil and gas pipelines.

Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford its versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Image reconstruction based on TV-L1 model for planar array capacitance defect detection

Planar array capacitive imaging is an emerging nondestructive testing (NDT) technology with broad application prospects. The severe ill-posed inverse problem of planar array capacitive imaging reduces quality of reconstructed images. In this paper, a total variation regularization model with an L1-norm (TV-L1 model) of 3 × 4 planar array capacitance imaging is established for imaging the defects detected in the inner layers of multi-layer composite components. Considering the solution instability caused by the non-differentiability of the TV-L1 model, a variable splitting self-adaptive augmented Lagrange alternating direction algorithm (VS-SALAD) is proposed. An augmented Lagrange method enhances model splitability, and a series of subproblems are obtained by variable splitting operation. The subproblems are solved iteratively using alternating minimization method with self-adaptive penalty parameter. Experimental results demonstrate that TV-L1 model effectively alleviates the ill-posed problem, and the proposed algorithm significantly improves both the quality of reconstructed images and solving speed of the TV-L1 model.

Defect identification method for capacitive-inductive dual-mode sensors with lift-off interference

The capacitive-inductive dual-mode probe provides a defect detection solution that allows simultaneous or sequential eddy current detection and capacitive imaging detection, known as capacitive-inductive dual-mode detection. The dual-mode detection combines the advantages of the two detection techniques and can be used to detect multiple types of defects in “insulator-conductor” multilayer hybrid structures. However, its high sensitivity poses a challenge as the probe's responses a susceptible to interference from lift-off variations, leading to non-relevant indications in the results that is not indicative of defects or anomalies. In this study, the Lift-off Characteristics (LoC) of the hybrid structures are studied, and an effective method for identifying relevant indications such as defect and anomaly for dual-mode sensor response signals with lift-off interference by combining the testing data of both detection modes and the LoC curve is proposed. A series of validation experiments were conducted to demonstrate the effectiveness of the proposed method in defect identification and classification. The results confirmed that the method effectively identified defect from lift-off interference and significantly reduced the occurrence of false judgments, thereby enhancing the reliability of the dual mode detection. Furthermore, the feasibility of utilizing the method to identify relevant indication when the sensor is tilted was also demonstrated.

Optimisation strategy for coplanar array capacitance imaging in rotational scanning mode

ABSTRACT Coplanar array capacitive imaging represents a non-destructive testing technology. To address the challenge of reduced utilisation of the high-sensitivity region caused by the ‘soft field’ effect characterised by severe nonlinear and inhomogeneous distribution of coplanar array capacitance sensitivity, resulting in difficulties in solving the problem of medium distribution, the proposed approach involves coplanar array capacitance sensor rotational scanning technology. This paper establishes both a physical rotation model and a sensitive field rotation model for a coplanar array capacitance sensor, demonstrating the inability to resolve the low-sensitivity region for medium distribution and illustrating how rotary scanning detection can enhance the utilisation rate of the high-sensitivity region. In conjunction with improved regional energy contrast, the images acquired under various rotation angles are fused, and experimental verification is conducted. The experimental results underscore that rotational scanning detection techniques can increase the utilisation rate of the high-sensitivity region within the coplanar array capacitance sensor by 74.69%. This enhancement effectively captures essential information within the reconstructed image and successfully addresses the challenge of solving difficulties in dielectric distribution. The research outcomes provide valuable insights for advancing non-destructive testing technologies employing coplanar array capacitance sensors.

The semi-analytical model of capacitive imaging (CI) sensors based on the renormalization group method

Purpose This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated. Design/methodology/approach The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing. Findings The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors. Originality/value The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.

A Discrimination method of three types of defects in insulated cladding components based on the lift-off effect of capacitive imaging (CI) technology

In insulated cladding components, simultaneously detecting and distinguishing surface defects on the insulation layer, hidden defects in the insulation layer, and surface defects on the metal layer of insulated cladding components can help in formulating appropriate maintenance plans and promptly eliminating safety hazards, which is of great significance for ensuring the safety of insulated cladding components in relevant application fields. This paper applies capacitive imaging (CI) to detect three different types of defects. A three-dimensional simulation model of the CI sensor detecting the three types of defects is established using COMSOL software. The corresponding measurement sensitivity distribution (MSD) and volume of influence (VOI) are obtained, and the detection mechanisms of the three different types of defects are analyzed in detail. By analyzing the lift-off height variation, the VOI regions' changing patterns for different types of defects are determined, and the distinguishing mechanisms for the three types of defects are obtained. Based on the monotonicity of the normalized variation ratio (NVR) and the differences in the positions of the intersection points, a comparative distinguishing method for the three types of defects based on the variation of the lift-off height is established. Experimental results demonstrate that the defect comparative distinguishing method based on the lift-off effect can effectively differentiate surface defects on the insulation layer, hidden defects in the insulation layer, and surface defects on the metal layer.

Experimental findings of underwater capacitive imaging

Experimental findings of underwater capacitive imaging

Evaluating Capacitive Imaging for Powder Bed Fusion Metal Additive Manufacturing

This work evaluates the use of co-planar capacitive sensors to provide layer-wise imaging results for the Powder Bed Fusion quality control. A new sensor was designed together with a digital readout electronics which allowed for high-resolution localized measurements. The system was evaluated in different surface features produced by laser PBF with stainless steel 316 powder. Features were tested with and without the presence of the original powder with results showing that the powder presence masked the output for most of the features. This suggests that the ability of carrying charges within an alternating electrical field is similar between powder and the consolidated metal, and that, the concerning signal contribution is much higher than that caused by the conductivity difference between powder and consolidated metal.

Sensitivity distribution optimization method for planar array capacitive imaging

Planar array capacitive imaging is an emerging detection technique that has the advantages of being non-invasive, having a fast response time, and the ability to approach an object being measured from one side. However, the presence of soft field effects leads to the non-uniform distribution of sensitivity and the existence of negative sensitivity regions caused by the presence of the guard electrodes, which affects the quality of image reconstruction. To address this problem, an iterative average pooling method for sensitivity distribution optimization is proposed. A three-dimensional model of the sensor is established, the initial sensitivity coefficient matrix is calculated using the finite element method and the distribution characteristics of the sensitivity matrix are analysed. Inspired by neural networks, the convolution operation is introduced into the original sensitivity matrix. Then the 3 × 3 convolution operation is utilized to extract sensitivity data features. Iterative average pooling is proposed to reduce the uneven distribution caused by the soft field effect. The strategy of using the average pooled sensitivity values as parameters in the next calculation is proposed to prevent sudden changes in the sensitivity matrix and ensure a consistent trend with the original matrix. The experimental results show that the proposed method can attenuate the influence of soft field effects and effectively improve the homogeneity of the sensitivity distribution and quality of image reconstruction.

Combined Sensor for Capacitive Imaging and Size Measurement for Surface Features

Capacitive imaging (CI) has been successfully used in nondestructive evaluation (NDE) applications. Conventional capacitive electrodes applied to CI technology are generally planar, and only one capacitor type exists in one probe. This work proposes a combined capacitive sensor, which is mainly composed of triangular prism electrodes and auxiliary electrodes, and measurement system for CI technology. Triangular prism electrodes are designed to improve the two-dimensional (2-D) image resolution compared to those of planar electrodes. Two different spatial arrangements of driving and sensing electrodes are applied to achieve the 2-D imaging and depth detection of specimen features. The auxiliary electrode is developed to measure the sizes of surface features and distinguish surface and internal features. Double scans and image superimposition are employed to address image blurring and shape distortion in a single direction of the image due to the different imaging resolutions of probes in two perpendicular directions. The feasibility of the combined sensor in feature imaging and key parameter detection is verified through experiments. This work expands the functionality of conventional capacitive probes by combining different electrodes in one probe and lays a foundation for the subsequent analysis and further processing of CI technology.

Skin Capacitive Image Stitching and Occlusion Measurements

The aim of this study is to develop new analysis techniques for skin capacitive image stitching and occlusion measurements. Through image stitching, small skin capacitive images can be stitched into large skin capacitive images and, therefore, provide more skin image information. Through occlusion, e.g., keeping the measurement device on skin for a period of time, the skin health status can be studied through time-dependent response curves. Results show that time-dependent skin capacitive imaging curves can tell us the information about transdermal water loss (TEWL) as well as skin surface profiles. By using the structural similarity index measure (SSIM), the TEWL map can be constructed, which shows the water loss map on the skin surface. We first present the theoretical background and then the experimental results.

Estimating Fingerprint Pose via Dense Voting

Aligning fingerprint images to a unified coordinate system defined by fingerprint pose is beneficial for fast and accurate fingerprint matching. Due to poor ridge quality and partial observations, however, performance of the state-of-the-art fingerprint pose estimation algorithms remains unsatisfactory. In this study, we propose to fuse voting strategy and deep network to estimate fingerprint center and direction. Rather than regressing them directly, we predict dense offset maps and vote for the final estimation. Experimental results on ten fingerprint datasets with over 60K fingerprints show that (1) highly consistent fingerprint pose estimations are obtained across different impressions of the same finger, (2) performance of fingerprint indexing and verification is further improved thanks to more accurate fingerprint pose estimation, and (3) the proposed approach is more robust to sensing technologies (optical, capacitive, inking, and direct imaging) and impression types (rolled, plain, latent, and contactless).

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.

Capacitive Sensors for Multiphase Flow Measurement: A Review

Capacitive sensors are found in numerous applications, and they are capable of sensing several parameters. Multiphase flows occur in many industrial processes that require proper monitoring to improve the efficiency and safety of plants and equipment. Detailed (high-resolution) flow information is required from dedicated flow experiments at a laboratory scale to develop and validate flow modelings. In both cases, capacitive flow sensors have been applied to have a varying degree of flow detailing, e.g., from simple capacitive probes up to capacitive flow imaging and tomography. This article reviews the different capacitive sensors applied to multiphase flow measurement and imaging. Operating principles, sensor geometries, and capacitive measuring circuits are presented and discussed along with advantages and disadvantages, focusing on flow applications and possible flow parameters yielded from sensors. Some new trends in capacitive flow sensors are also presented.

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.

A threshold optimization strategy for reducing the noise of co-planar array capacitive imaging

Co-planar array capacitive imaging is a non-destructive testing technique based on the sensing of dielectric properties. As the number of independent electrodes in the sensor grows, handling noise becomes more and more critical. In this paper, numerical models and practical tests were used to explore the noise analysis and processing of the output signals of a 12-electrode co-planar array capacitive sensor. The effects of different geometric placements and distances on the output signal strength and sensitivity of electrode pairs were thoroughly assessed, revealing that distal interpolar spacing electrode pairs were particularly vulnerable to noise. As a consequence, a threshold optimization strategy was presented for decreasing the noise. Results revealed that threshold optimization efficiently reduced noise and emphasized the useful information for image reconstruction. The artifacts and deviations of the reconstructed images were reduced, and the structural similarity and Pearson product-moment correlation coefficients were boosted. The methodologies and findings may provide a reference for signal processing in co-planar array capacitive imaging.

Identification of defects in the inner layers of composite components based on capacitive sensing.

This paper describes the development and validation of a rapid internal defect detection method for multilayer composite components. Coplanar array capacitive imaging is based on electrical capacitance tomography, in which all electrodes are arranged in a single plane. The coplanar array capacitive sensor system is based on the capacitive edge effect and reconstructs the dielectric distribution in the sensitive area by measuring the capacitance of the sensor. A 4 × 3 array of coplanar electrode sensors is established and used to image the defects in the inner layers of multilayer composite components. Using a 3D model of the sensor and the sensitivity field, the variation pattern of the sensitivity field is analyzed. By placing different objects into the sensitivity area of the system, changes in the dielectric constant can be observed. Multilayer composite components with void defects are placed in the measurement area for defect detection. The dielectric distribution is visualized by reconstruction algorithms from the capacitance data and sensitivity field data. The results show that the imaging system based on a coplanar array capacitive sensor can reproduce the location of defects and realize the nondestructive testing of complex multilayer composite components.

Robust defect detection under uncertainties using spatially adaptive capacitive imaging

We develop a high-Q capacitive sensing based robust non-destructive evaluation (NDE) methodology that can be widely used in varied NDE applications. We show that the proposed method can detect defects in a host of robust regimes where uncertainties such as lift-off, probe tilt, fluctuations in sampling rates, and step sizes are inherent in the data collection process. We explicitly characterize the corruption in the capacitive sensing data due to various lift-off based uncertainties. We use a Bayesian decision theoretic approach to rigorously understand the impact of these corruptions on defect identification efficacy. Using an optimally tuned weighted classification loss, we prove that it is theoretically feasible to accurately detect defect location and sizes from capacitive sensing signals collected under the aforementioned uncertainties. The Bayesian decision theoretic study needs prior information for accurate detection that is not available in real NDE inspections. So, we develop a solely data driven algorithm that analyzes the capacitive sensing signals without any prior knowledge of defect or uncertainty types. The developed algorithm is non-parametric and uses spatially adaptive denoising to weed out uncertainty induced noises. By leveraging the spatial association in the capacitive sensing signals, our algorithm greatly improves on popular non-spatial approaches. Compared to popular thresholding methods and low-rank based denoising approaches, we demonstrate superior performance of the proposed method in terms of coverage and false positive metrics for defect identification. Using spatially adaptive denoising, we design a robust capacitive sensing method that can detect defects with high precision under various uncertainty regimes.