• Structural Monitoring and Maintenance
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• v.3 no.4
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• pp.335-347
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• 2016

One of major errors in flow rate measurement for two-phase flow using an Electrical Capacitance Sensor (ECS) concerns sensor sensitivity under temperature raise. The thermal effect on electrical capacitance sensor (ECS) system for air-water two-phase flow monitoring include sensor sensitivity, capacitance measurements, capacitance change and node potential distribution is reported in this paper. The rules of 12-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance and sensitivity map the basis of Air-water two-phase flow permittivity distribution and temperature raise are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. The cross-sectional void fraction as a function of temperature is determined from the scripting capabilities in ANSYS simulation. The results show that the temperature raise had a detrimental effect on the electrodes sensitivity and sensitive domain of electrodes. The FE results are in excellent agreement with an experimental result available in the literature, thus validating the accuracy and reliability of the proposed flow rate measurement system.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.544-546
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• 2000

This paper presents a monitoring technique of pole transformers using AC bridge circuit. And to detect the capacitance in oil more effectively, this paper used AC bridge circuit. The sensor in oil which could be placed inside of the distribution transformer can measures the changes of capacitance in oil. And with the sensing of the upper part's capacitance, it is possible to determine the changes of the oil height. Establishment of the proposed system helps to build the confidence in monitoring of the pole transformers.

• Tribology and Lubricants
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• v.27 no.3
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• pp.125-133
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• 2011

The purpose of this study is to develop an engine oil quality monitoring system to warn the abnormal condition of engine oil. To do this, first of all, it is needed a personal controller development to measure the capacitance of a pre-developed engine oil deterioration detection sensor integrated with an oil filter. To measure the capacitance of engine oil in the sensor, it is used the way measuring the electric charging time in a capacitor by impressing DC volt. This method has merits on cost and signal stability. The measured capacitance is compensated by comparing with the one measured by an impedance analyzer. Also, using the dielectric constant gained by an impedance analyzer, the calculating equation of the dielectric constant of engine oil related with the currently developed sensor is decided. Then, the deterioration degree of engine oil is estimated according to the change rate of dielectric constant between green oil and used oil. Finally, using this dielectric constant information together with engine oil temperature and pressure, the currently developed engine oil quality monitoring system is to tell the abnormal state of engine oil.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.7
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• pp.1563-1572
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• 1995

The on-line cure monitoring during the cure process of fiber reinforced resin matrix composite material is important for the better quality and productivity. Among several cure monitoring methods, the dielectrometry that uses electrodes as its sensor is known to be the most promising method. In this study, the sensitivity of the dielectric sensor for the on-line cure monitoring was analyzed by finite element method and compared to the experimental results. Using the analytical results, the equation for the capacitance of the sensor was derived. Also, the optimal sensor design method was suggested after analyzing several different sensor shapes.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.1
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• pp.62-65
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• 2019

Wireless power transfer systems require an algorithm to determine the presence of the target object for mitigating standby power and safety issues. Although many schemes that sense various external objects have been actively proposed for inductive power transfer systems, not many studies on capacitive power transfer systems have been conducted compared with those on inductive power transfer systems. This study proposes a target object detection algorithm by monitoring the capacitance in transmitter-side electrodes without additional pressure sensors or distance sensors. The proposed algorithm determines the presence of a target object by monitoring the change in capacitance in transmitter-side electrodes using the step pulse of the microcontroller unit. The algorithm is verified by two step processes. First, the performance in capacitance measurement is compared with that of an LCR meter. Then, the verification is conducted in a 5-W capacitive power transfer hardware. Experimental result shows that the interelectrode capacitance increases by 6 times when the target object is fully aligned. Thus, the proposed scheme can successfully detect the presence of the target object.

• Journal of Powder Materials
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• v.30 no.4
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• pp.310-317
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• 2023

Small-film-type ion sensors are garnering considerable interest in the fields of wearable healthcare and home-based monitoring systems. The performance of these sensors primarily relies on electrode capacitance, often employing nanocomposite materials composed of nano- and sub-micrometer particles. Traditional techniques for enhancing capacitance involve the creation of nanoparticles on film electrodes, which require cost-intensive and complex chemical synthesis processes, followed by additional coating optimization. In this study, we introduce a simple one-step electrochemical method for fabricating gold nanoparticles on a carbon nanotube (Au NP-CNT) electrode surface through cyclic voltammetry deposition. Furthermore, we assess the improvement in capacitance by distinguishing between the electrical double-layer capacitance and diffusion-controlled capacitance, thereby clarifying the principles underpinning the material design. The Au NP-CNT electrode maintains its stability and sensitivity for up to 50 d, signifying its potential for advanced ion sensing. Additionally, integration with a mobile wireless data system highlights the versatility of the sensor for health applications.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.345-350
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• 2014

DC electrolytic capacitor is widely used in the power converter including PWM inverter, switching power supply and PFC Boost converter system because of its large capacitance, small size and low cost. In this paper, basic characteristics of DC electrolytic capacitor vs. frequency is presented and the real-time estimation scheme of ESR and capacitance based on the bandpass filtering is adopted to the single phase boost converter of uninterruptible power supply to diagnose its split dc-link capacitors. The feasibility of this real-time failure prediction monitoring system is verified by the computer simulation of the 5[kW] singe phase PFC half-bridge boost converter.

• Advances in nano research
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• v.9 no.1
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• pp.1-13
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• 2020

In this work, the electrical potential (EP) technique with an artificial neural networks (ANNs) for monitoring of nanostructures are used for the first time. This study employs an expert system to identify size and localize hidden nano-delamination (N.Del) inside layers of nano-pipe (N.P) manufactured from Basalt Fiber Reinforced Polymer (BFRP) laminate composite by using low-cost monitoring method of electrical potential (EP) technique with an artificial neural networks (ANNs), which are combined to decrease detection effort to discern N.Del location/size inside the N.P layers, with high accuracy, simple and low-cost. The dielectric properties of the N.P material are measured before and after N.Del introduced using arrays of electrical contacts and the variation in capacitance values, capacitance change and node potential distribution are analyzed. Using these changes in electrical potential due to N.Del, a finite element (FE) simulation model for N.Del location/size detection is generated by ANSYS and MATLAB, which are combined to simulate sensor characteristic, therefore, FE analyses are employed to make sets of data for the learning of the ANNs. The method is applied for the N.Del monitoring, to minimize the number of FE analysis in order to keep the cost and save the time of the assessment to a minimum. The FE results are in excellent agreement with an ANN and the experimental results available in the literature, thus validating the accuracy and reliability of the proposed technique.

• Structural Monitoring and Maintenance
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• v.3 no.3
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• pp.297-314
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• 2016

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 (${\theta}$) 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.

• Journal of Energy Engineering
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• v.11 no.1
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• pp.67-73
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• 2002

The carbon-in-ash system for simultaneously monitoring the unburned carbon in fly ash produced in a coal-fired power plan is designed and evaluated using the domestic fly ash produced in the coal-fired power plant. Real time monitoring is very important to control the combustion of the boiler in the coal-fired power plant and the purification system for fly ash recycling. The carbon-in-ash system based on the capacitance measurement consists of a LCR meter, a duct collector and an electrode cell. The capacitance of fly ash increases linearly with increasing fly ash carbon contents. The water content in fly ash plays an important role on the ash capacitance. The empirical equation for predicting the content of unburned carbon in fly ash produced in the domestic Boryung, Hadong and Samchenpo coal-fired power plants can be derived in the range of carbon content 0-20%.