Electrical Capacitance Sensor Research Articles

A comprehensive study of a long-term creep thermo-mechanical fatigue behavior monitoring of BFRP composite pipeline using electrical capacitance sensors and deep learning algorithm

The composite pipeline is a relatively new and viable alternative pipeline to the more commonly used traditional one due to its good mechanical and fatigue properties and lower production cost. For this purpose, it is critical to assess the mechanical and fatigue performance of composite pipeline material under various working conditions, particularly for monitoring long-term creep thermo-mechanical fatigue behavior. In this paper, a long-term creep thermo-mechanical fatigue behavior in a basalt fiber reinforced polymer laminated composite pipeline is detected through an integrated expert system consisting of the electrical capacitance sensors and a deep learning algorithm. First, a multi-physics finite element model is established for the simulation of a long-term creep thermo-mechanical fatigue behavior in basalt fiber reinforced polymer composite pipelines subjected to long-term fatigue loading of internal pressure and thermal effect. Second, theoretical model results of long-term creep thermo-mechanical fatigue compliance (Sf(t)) over the time of creep are analyzed in pipeline material using the modulus degradation approach. Finally, an electrical potential change between electrical capacitance sensors electrodes corresponding to Sf(t) over the time of creep for some levels of long-term creep thermo-mechanical fatigue (Rf%) is recorded and then used in these datasets for training of the novel deep neural network based on one of the most widely used of the deep neural network families is the convolutional neural network, to predict Sf(t) in pipeline for various Rf% not included in the previous finite element model evaluation (i.e. electrical capacitance sensors technique). In this paper first is detected the long-term creep thermo-mechanical fatigue behavior for Rf%=25%,50%, and 75% from a finite element model and modulus degradation approach, and then is predicted the long-term creep thermo-mechanical fatigue behavior for Rf%=15%,35%,60%, and 85% respectively via deep neural network. The proposed method results are in good agreement with the experimental results available in the literature, thus verifying the accuracy and reliability of the proposed technique and its applicability to other different composite structures.

A new diagnostic system for damage monitoring of BFRP plates

This work studies a new framework for damage detection in composite plate systems made of Basalt Fiber Reinforced Polymer (BFRP) through analysis of electrical capacitance sensors (ECS) measurements. First, the plate damage finite element model is established, where one side of the plate is subjected to the fatigue effect of applied pressure, then the electrical potential difference between the electrode pairs (EPD) is measured before and after damage. The distributed ECS measuring electrodes are installed around the Plate and loaded the transient external excitations between each other. The transfer function (TF) of the “open loop” plate system is suggested and applied to reflect damage evolution. The accuracy and reliability of the proposed technique are validated with available experimental results in the literature. Results show that the signal magnitude will suddenly and sensitively change when the damage starts to grow under electrode pairs, which shows the effectiveness of the proposed approach and promising potential for engineering applications.

A deep-learning approach for predicting water absorption in composite pipes by extracting the material’s dielectric features

Monitoring the mass of liquid absorption in laminated composite structures, that have a direct contact surface with working liquid-like pipes, is very important in order to prevent the sudden collapse of the structures because of the degradations in strength and mechanical properties over time. An electrical capacitance sensor technique has been applied for monitoring the mass of liquid absorption, over the time, in laminated composite pipelines by measuring the change of dielectric characteristics of composite pipelines subjected to an internal hydrostatic pressure load of water and thermal effect. Results show the technique is very effective. However, a major difficulty in utilizing this technique is that it is highly time consuming to be used for monitoring, in addition to the detection efforts that it requires to calculate the mass of liquid absorption that exerts high cost and loss of additional time in monitoring. In this paper, a deep neural network model is used to estimate the mass of liquid absorption in glass fiber reinforced epoxy laminated composite pipelines by extracting the features from datasets from experimental and numerical measurements of the electrical capacitance sensor. The experimental and numerical data used in this paper to train and test the new deep neural network model are collected from the literature and the finite element model of the electrical capacitance sensor system respectively. The results show an excellent agreement between the finite element model data, available experimental data, and those predicted by a deep neural network with an average error of 0.067%, and show that the proposed method achieves satisfactory performance with 86.34% accuracy, 82.83% regression rate and 83.74% F-score. The proposed approach overcomes the difficult problem of saving time and effort to accurately detect the mass of liquid absorption over the time, and provides a promising approach for a wider application of this intelligent model.

Enhanced Multiphase Flow Measurement Using Dual Non-Intrusive Techniques and ANN Model for Void Fraction Determination

There are many petrochemical industries that need adequate knowledge of multiphase flow phenomena inside pipes. In such industries, measuring the void fraction is considered to be a very challenging task. Thus, various techniques have been used for void fraction measurements. For determining more accurate multiphase flow measurements, this study employed dual non-intrusive techniques, gamma-ray and electrical capacitance sensors. The techniques using such sensors are considered non-intrusive as they do not cause any perturbation of the local structure of the phases’ flow. The first aim of this paper is to analyze both techniques separately for the void fraction data obtained from practical experiments. The second aim is to use both techniques’ data in a neural network model to analyze measurements more efficiently. Accordingly, a new system is configured to combine the two techniques’ data to obtain more precise results than they can individually. The simulations and analyzing procedures were performed using MATLAB. The model shows that using gamma-ray and capacitance-based sensors gives Mean Absolute Errors (MAE) of 3.8% and 2.6%, respectively, while using both techniques gives a lower MAE that is nearly 1%. Consequently, measurements using two techniques have the ability to enhance the multiphase flows’ observation with more accurate features. Such a hybrid measurement system is proposed to be a forward step toward an adaptive observation system within related applications of multiphase flows.

Sectional water fraction measurement for gas-water two-phase flow containing a conductive water phase utilizing capacitance sensor

By applying an electrical capacitance sensor to sectional water fraction measurement for gas–water (salinity) two-phase flow, this paper investigates the effect of a medium’s conductivity on the measurement of capacitance sensors and its elimination. Firstly, the mechanism of how a medium’s conductivity influences the capacitance measurement of a capacitance sensor is analyzed by combining a physical model of the sensor with its conditioning circuit. The theoretical analysis indicates that the applied exciting frequency is a key factor influencing the conductivity effect; the effect can be restrained by increasing the excitation frequency. Then, an exciting frequency optimization rule is proposed accordingly. In addition, sensor configuration and geometry optimization (based on finite element method simulations) are discussed to achieve better performance. Dynamic and static calibration experiments are carried out for stratified gas–liquid (salinity) two-phase flow for the purpose of validating the proposed method. The experimental results confirm the effectiveness of the proposed method. This work is constructive to the design of capacitive sensing technologies applied to conductive objects, and offers insights into the structural design and optimization of capacitance sensors.

Tensile creep monitoring of basalt fiber-reinforced polymer plates via electrical potential change and artificial neural network

In this research, the long-term tensile creep (LTTC) failure in basalt fiber reinforced polymer (BFRP) composites under ambient conditions was detected and predicted via an expert system, in order to monitor the LTTC of BFRP laminate composites. This was accomplished by using the electrical potential change (EPC) technique that employs an electrical capacitance sensor (ECS) in conjunction with an artificial neural network (ANN). A finite element (FE) simulation model for tensile creep detection is generated by ANSYS and MATLAB. Therefore, FE analyses are employed to obtain groups of data for the training of the ANNs. The proposed method is applied to minimize the number of FE analysis for keeping the cost down and save the time of the creep behavior monitoring to a minimum. The paper first presents a study on the creep monitoring for different levels of tensile creep (%σc) as a percentage of ultimate tensile strength (UTS) equal to (25%, 50% and 75%) using EPC technique. Subsequently, the trained ANN is utilized to predict the creep behavior for the level of %σc not included in the FE data. Four different values are selected for the level of %σc; (15, 35%, 60% and 85%).

Systematic Frequency and Statistical Analysis Approach to Identify Different Gas–Liquid Flow Patterns Using Two Electrodes Capacitance Sensor: Experimental Evaluations

This work proposes a method to distinguish between various flow patterns in a multiphase gas–liquid system. The complete discrimination between different flow patterns can be achieved by mapping the corresponding frequency and statistical parameters. These parameters are usually obtained from further analysis conducted on the signal data of the utilized sensor. The proposed technique is based on establishing interrelationships between these parameters, namely the mean (m), the standard deviation ( σ ¯ ), power spectral density (PSD), the width of the characteristic frequency peaks (Δƒ), the skewness ( γ 1 ) and the kurtosis ( γ 2 ). Therefore, a relatively simple electrical capacitance sensor with two electrodes was designed and implemented on a two-phase flow apparatus with a circular pipe. The experimental operating conditions comprised of different combinations of air–water superficial velocities at three inclinations (i.e., horizontal, upward 15° and upward 30°). This research discusses in specific the analysis underlying flow patterns identification method and the rationale for selecting the proposed approach. The results showed that some parameters found to be more valuable than others such as m, σ ¯ and Δƒ. Besides, combining two sets of these statistical graphs which are (a) σ ¯ vs. Δƒ with Δƒ vs. m (or Δƒ vs. total power), (b) Δƒ vs. total power with γ 1 vs. σ ¯ (or γ 2 vs. σ ¯ ), and (c) σ ¯ vs. m with Δƒ vs. m (or Δƒ vs. total power), allowed all flow patterns field to be identified clearly at all inclinations. It is therefore concluded that for any gas–liquid multiphase flow system, the reported approach can be used reliably to discriminate between different generated flow patterns.

Design of capacitance sensor for two phase flow monitoring based on finite element models

Monitoring water-oil two-phase flow is of great industrial significance. In this work, an electrical capacitance sensor was designed and tested to monitor the two-phase flow. In order to optimise the capacitance sensor for two-phase flow monitoring, it is necessary to analyse capacitance sensors with different geometries. In this paper, the capacitance sensors with concave plates, double ring electrodes, helical electrodes, parallel strips and perpendicular strips are proposed and briefly introduced. The Finite Element (FE) models for capacitance sensors were constructed in COMSOL Multiphysics, and the preliminary experiments were carried out. Both simulation and experimental results show that the capacitance of the sensor tends to increase with the decreased oil content. The measurement sensitivity distributions obtained from FE models vary with different electrode geometries, based on which design parameters can then be selected to address different requirements.

Design of capacitance sensor for two phase flow monitoring based on finite element models

Monitoring water-oil two-phase flow is of great industrial significance. In this work, an electrical capacitance sensor was designed and tested to monitor the two-phase flow. In order to optimise the capacitance sensor for two-phase flow monitoring, it is necessary to analyse capacitance sensors with different geometries. In this paper, the capacitance sensors with concave plates, double ring electrodes, helical electrodes, parallel strips and perpendicular strips are proposed and briefly introduced. The Finite Element (FE) models for capacitance sensors were constructed in COMSOL Multiphysics, and the preliminary experiments were carried out. Both simulation and experimental results show that the capacitance of the sensor tends to increase with the decreased oil content. The measurement sensitivity distributions obtained from FE models vary with different electrode geometries, based on which design parameters can then be selected to address different requirements.

Fatigue damage identification for composite pipeline systems using electrical capacitance sensors

This research investigates a damage identification framework for carbon fiber reinforced polymer composite pipeline systems using electrical capacitance sensors. First, a finite element pipeline model is established under the structural-thermal-electrostatic coupled field. Based on this model, an accumulative damage model is introduced to model the pipeline damage subjected to fatigue effect, and ten damage cases are considered. Both static and transient external excitations are loaded into the pipeline by installing distributed electrical capacitance sensors. A system transfer function is proposed and applied to the ‘open loop’ pipeline system. Results show these approaches perform great promises when damage evolves covering electrode pairs, the signal will change sensitively and abruptly with an order of magnitude.

Monitoring the water absorption in GFRE pipes via an electrical capacitance sensors

One of the major problems in glass fiber reinforced epoxy (GFRE) composite pipes is the durability under water absorption. This condition is generally recognized to cause degradations in strength and mechanical properties. Therefore, there is a need for an intelligent system for detecting the absorption rate and computing the mass of water absorption (M%) as a function of absorption time (t). The present work represents a new non-destructive evaluation (NDE) technique for detecting the water absorption rate by evaluating the dielectric properties of glass fiber and epoxy resin composite pipes subjected to internal hydrostatic pressure at room temperature. The variation in the dielectric signatures is employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such defects. ECS consists of twelve electrodes mounted on the outer surface of the pipe. Radius-electrode ratio is defined as the ratio of inner and outer radius of pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes on the basis of water absorption rate in the pipe material as a function of absorption time. The arrangements for positioning12-electrode sensor parameters such as capacitance, capacitance change and change rate of capacitance are analyzed by ANSYS and MATLAB to plot the mass of water absorption curve against absorption time (t). An analytical model based on a Fickian diffusion model is conducted to predict the saturation level of water absorption (MS) from the obtained mass of water absorption curve. The FE results are in excellent agreement with the analytical results and experimental results available in the literature, thus, validating the accuracy and reliability of the proposed expert system.

Single-Plane Dual-Modality Tomography for Multiphase Flow Imaging by Integrating Electrical Capacitance and Ultrasonic Sensors

This paper presents an experimental study of a single-plane dual-modality tomography (DMT) system integrating electrical capacitance tomography (ECT) and ultrasonic tomography (UT) for water–oil–gas three-phase flow imaging. The soft-field (ECT) and hard-field (UT) systems measure different physical parameters, making them both a fine combination in complementing each system’s limitations. The DMT system consists of 16 units of customized ECT sensors and 16 units of UT transducers, which are composited on a single-plane arrangement to perform concurrent measurement. A finite-element method is used to model and study its feasibility and performance before engaging with the actual hardware. The DMT system is carried out on a vertical test column and horizontal stratified flow to obtain sensor data from each modality by inspection of separate tomographic image using linear back-projection (LBP) image reconstruction algorithm and convolution back projection with Gaussian filtering (CBPF). A fuzzy logic pixel fusion method is used for image fusion process. To evaluate the reconstructed images, the mean structural similarity index (MSSIM) method and the mutual information method ( $M_{F})$ are employed on both the LBP and CBPF image results. The analysis and assessments proved that the CBPF method has successfully improvised the image results and thus has better MSSIM rating and $M_{F}$ over the LBP method with an enhancement up to 12.55% and 8.92%, respectively.

Detection of Fatigue Crack in Basalt FRP Laminate Composite Pipe using Electrical Potential Change Method

Novel modulation electrical potential change (EPC) method for fatigue crack detection in a basalt fibre reinforced polymer (FRP) laminate composite pipe is carried out in this paper. The technique is applied to a laminate pipe with an embedded crack in three layers [0º/90º/0º]s. EPC is applied for evaluating the dielectric properties of basalt FRP pipe by using an electrical capacitance sensor (ECS) to discern damages in the pipe. Twelve electrodes are mounted on the outer surface of the pipe and the changes in the modulation dielectric properties of the piping system are analyzed to detect damages in the pipe. An embedded crack is created by a fatigue internal pressure test. The capacitance values, capacitance change and node potential distribution of ECS electrodes are calculated before and after crack initiates using a finite element method (FEM) by ANSYS and MATLAB, which are combined to simulate sensor characteristics and fatigue behaviour. The crack lengths of the basalt FRP are investigated for various number of cycles to failure for determining crack growth rate. Response surfaces are adopted as a tool for solving inverse problems to estimate crack lengths from the measured electric potential differences of all segments between electrodes to validate the FEM results. The results show that, the good convergence between the FEM and estimated results. Also the results of this study show that the electrical potential difference of the basalt FRP laminate increases during cyclic loading, caused by matrix cracking. The results indicate that the proposed method successfully provides fatigue crack detection for basalt FRP laminate composite pipes.

The thermal effect on electrical capacitance sensor for two-phase flow monitoring

The thermal effect on electrical capacitance sensor for two-phase flow monitoring

FE and ANN model of ECS to simulate the pipelines suffer from internal corrosion

As the study of internal corrosion of pipeline need a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new non-destructive evaluation (NDE) technique for detecting the internal corrosion inside pipeline by evaluating the dielectric properties of steel pipe at room temperature by using electrical capacitance sensor (ECS), then predict the effect of pipeline environment temperature (o) on the corrosion rates by designing an efficient artificial neural network (ANN) architecture. ECS consists of number of electrodes mounted on the outer surface of pipeline, the sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of two dimensional capacitance sensors are illustrated. The variation in the dielectric signatures was employed to design electrical capacitance sensor (ECS) with high sensitivity to detect such defects. The rules of 24-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. A feed-forward neural network (FFNN) structure are applied, trained and tested to predict the finite element (FE) results of corrosion rates under room temperature, and then used the trained FFNN to predict corrosion rates at different temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an FFNN results, thus validating the accuracy and reliability of the proposed technique and leads to better understanding of the corrosion mechanism under different pipeline environmental temperature.

3D MODELLING OF ELECTRICAL CAPACITANCE AND ULTRASONIC SENSOR INTEGRATION USING FINITE ELEMENT METHOD

This paper presents a three dimensional (3D) modelling of a dual modality sensor composite which emerges electrical capacitance sensor (ES) and ultrasonic transducer (UT) for process tomography applications. The dual-modality tomography (DMT) setup has employed a customized ES electrodes along with UT on a single-plane. A finite element method is used to analyze the feasibility and the behavior of the DMT setup in 3D environment using COMSOL Multiphysics software. This simulation is performed both in a homogeneous and inhomogeneous conditions to investigate the wave distribution and its pattern thus the simulation results are presented in this paper.

Electrical capacitance sensor array to measure density profiles of a vibrated granular bed

Monitoring the dynamical evolution of the solids distribution within vibrating granular beds is of key importance to control a variety of industrial processes and to assist basic studies on phenomena like segregation and convection. One of the most used techniques is video analysis, however it is restricted to quasi-two dimensional beds and may need considerable postprocessing time. Here we explore an alternative technique based on a simple linear capacitor array that is able to resolve variations of the solid fraction along a single direction. This method has potential to be used in real-time control applications due to the simple signal processing needed and it’s suited to probe three dimensional beds. We show a comparison between spatiotemporal density profiles of a quasi two dimensional vertically vibrated bed obtained with video and capacitive methods, and discuss on the advantages and downsides of each one. We also present spatiotemporal density profiles of a three dimensional vertically vibrated bed using the capacitive technique, illustrating the distribution of material along the direction of vibration when the bed performs bouncing motion and when the forcing is enough to obtain a granular Leidenfrost effect.

Image Fusion Using Fuzzy Logic Pixel Fusion for Dual Modality Tomography System

Process tomography has been widely investigated and developed over time. The aim of this method is to measure the cross-sectional distribution of multiphase flow parameters based on its sensing technique. These single modality approaches has its own limitation whereby combination of two or more modality could overcome this issue. Therefore, a dual modality tomography (DMT) emergence of electrical capacitance sensors and ultrasonic sensor arrays for multiphase liquid mixture measurement was proposed. This paper presents an image fusion model using fuzzy logic pixel fusion method to integrate information for multisensory system such as DMT. A simulation analysis was carried out to perform image fusion on both ECT and UT input images. The developed fusion model proven to be successful in visualizing multiphase flow measurement. Thus, further validation and assessments were employed on the acquired results.

Effects of particle charging on electrical capacitance tomography system

A multimodal tomography system based on the electrical capacitance and electrostatic sensor arrays is proposed to investigate the effects of particle charging in the pipe on electrical capacitance tomography (ECT). The effect of the charge in the pipe on the detection circuit of ECT is firstly analyzed. Then the simulations are performed to make further investigation on the effect of the charge distribution on ECT. The simulation results indicate that the electrostatic effects on ECT vary with the spatial distribution of the charge in the pipe. The charges near the pipe wall have greater effect on ECT than those in the center of the pipe. The capacitance electrode-pairs near the charge distribution are greatly affected. Experiments are also performed on a pulley rig, which verify the simulation results.

Dual-mode capacitance and gamma-ray tomography using the Landweber reconstruction algorithm

A dual-mode tomography system based on electrical capacitance and gamma-ray tomography has been developed at the Department of Physics and Technology, University of Bergen. The objective of the dual-mode tomograph is to acquire cross-sectional images, i.e. tomograms, of hydrocarbon flow comprising oil, water and gas constituents. The capacitance tomograph utilizes an eight-electrode sensor set-up mounted around a PVC pipe structure which is sensitive to the electrical permittivity εr of the fluid. By using the capacitance tomograph, the produced water constituent can be separated from the gas and crude oil constituents, assuming that the liquid phase is oil continuous. The high-speed gamma-ray tomograph comprises five 500 mCi 241Am gamma-ray sources, each at a principal energy of 59.5 keV, which corresponds to five detector modules, each consisting of 17 CdZnTe detectors mounted around the same pipe section as the capacitance sensor. The gamma-ray tomograph discriminates between the gas and the liquid phase, since these have different photon attenuation properties. As a result, the gamma-ray tomograph is able to distinguish the gas phase from the liquid phase of the hydrocarbon flow. Consequently, the dual-mode capacitance and gamma-ray tomography set-up is able to distinguish the oil, water and gas constituents of hydrocarbon flow. This paper presents the work that has been performed related to static characterization of the dual-mode tomograph using the Landweber reconstruction algorithm on polypropylene phantoms. The objective of the work has been to quantitatively evaluate the static imaging performance of the dual-mode tomograph with respect to relative spatial measurement errors, i.e. root mean square errors of the reconstructed tomograms compared to that of the phantom. The work shows that dual-mode tomography using electrical capacitance and gamma-ray sensors is feasible on hydrocarbon flow components using a pixel-to-pixel fusion procedure on separately reconstructed tomograms based on the Landweber reconstruction algorithm.