• Tribology and Lubricants
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• v.20 no.5
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• pp.231-236
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• 2004

Sulfur Hexafluoride, S $F_{6}$ is widely used for leak detection and as a gaseous dielectric in transformers, condensers and circuit breakers. S $F_{6}$ gas is also effective as a cleanser in the semiconductor industry. This paper presents a numerical study of the sealing force of disk type seal in S $F_{6}$ gas safety valve. The sealing force on the disk seal is analyzed by the FEM method based on the Taguch's experimental design technique. Disk seals in S $F_{6}$ gas safety valve are designed with 9 design models based on 3 different contact length, compressive ratio and gas pressure. The calculated results of Cauchy stress and strain showed that the sealing characteristics of Teflon $^{ }$PTFE is more effective compared to that of FKM(Viton), which is related to the stiffness of the materials. And also, the contact length of the disk seal is important design parameter for sealing the S $F_{6}$ gas leakage in the safety valve.afety valve.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2198-2202
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• 2005

In this paper we have presented a condition monitoring method of check valve using neural network. The acoustic emission sensor was used to acquire the condition signals of check valve in direct vessel injection (DVI) test loop. The acquired sensor signal pass through a signal conditioning which are consisted of steps; rejection of background noise, amplification, analogue to digital conversion, extract of feature points. The extracted feature points which represent the condition of check valve was utilized input values of fault diagnosis algorithms using pre-learned neural network. The fault diagnosis algorithm proceeds fault detection, fault isolation and fault identification within limited ranges. The developed algorithm enables timely diagnosis of failure of check valve’s degradation and service aging so that maintenance and replacement could be preformed prior to loss of the safety function. The overall process has been experimented and the results are given to show its effectiveness.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.105-108
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• 1994

A comparative study was made of the valvular sounds produced by normal prosthetic valves with thrombosed prosthetic valves. Comparisons of the closing sound were made for the power frequency spectra associated with individual valves. We used periodogram approach to obtain the spectral characteristics of the valve. Spectral analysis system was tested in mock circulatory system by comparing normal valves with those produced by the same valves but having simulated thrombosis at the hinge of the valve. The heart sounds was recorded from two patients having normal mechanical valve and thrombosed mechanical valve. The estimated spectrum of the thrombosed mechanical valve displayed lower apparent peak frequency than that of the normal valve. The results showed that frequency spectra gave information pertinent to the valve malfunction. Sound spectral analysis is simple and alternative diagnostic tool for early detection of prosthetic valve mal function.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.77-86
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• 2015

It is difficult to extract small valve automatically in noisy 3D point cloud obtained from LIDAR because small object is affected by noise considerably. In this paper, we assume that the valve is a complex model consisting of torus, cylinder and plane represents handle, rib and center plane to extract a pose of the valve. And to extract the pose, we received additional input: center of the valve. We generated histogram of distance between the center and each points of point cloud, and obtain pose of valve by extracting parameters of handle, rib and center plane. Finally, the valve is reconstructed.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.1-5
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• 2013

The two major problems related to the blood flow in replaced prosthetic heart valve are thrombus formation and hemolysis. Reliability of prosthetic valve is very important because its failure means the death of patient. There are many factors affecting the valvular failures and their representatives are mechanical failure and thrombosis, so early noninvasive detection is essentially required. The purpose of this study is to detect the various thromboses formation by using acoustic signal acquisition and its spectral analysis on the frequency domain. We made the thrombosis models using Polydimethylsiloxane (PDMS) and they are thrombosis model on the disc, around the sewing ring and fibrous tissue growth across the orifice of valve. Using microphone and amplifier, we measured the acoustic signal from the prosthetic valve, which is attached to the pulsatile mock circulation system. A/D converter sampled the acoustic signal and the spectral analysis is the main algorithm for obtaining spectrum. Then the spectrum of normal and 5 different kinds of abnormal valve were obtained. Each spectrum waveform shows a primary and secondary peak. The secondary peak changes according to the thrombus model. To quantitatively distinguish the frequency peak of the normal valve from that of the thrombosed valves, analysis using a neural network was employed. Acoustic measurement has been used as a noninvasive diagnostic tool and is thought to be a good method for detecting possible mechanical failure or thrombus.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.417-422
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• 2017

One of the most frightening aspects of weapons of mass destruction (WMD) is their ability to cause death in very small quantities without being visible to the public. The military authorities are making considerable effort to ensure the survivability of the combatants in the event of NBC(Nuclear, Biological and Chemical) contamination. Therefore, in this study, modules were developed for the measurement of the positive pressure and for the detection of the chemicals used for the control of the various shut-off valves used in an NBC shelter. In addition, a high performance gas tight shut-off valve was developed that can overcome the disadvantages associated with manual manufacturing, such as the occurrence of defective products and high manufacturing cost. By applying the positive pressure measurement and chemical detection modules, this valve was able to be used to control the facility. The developed gas-tight shut-off valve maintained airtight characteristics at a pressure loss of 28[Pa] at the prescribed wind velocity and an internal pressure of 30[kPa]. It is expected to be possible to control the gas-tight shut-off valve through the remote measurement of the positive pressure, thereby ensuring the foreign independence of import substitution and defense related technology in the future. In addition, by installing these valves in all of the intake ports or exhaust ports connected to the outside of the NBC shelter, it is possible to prevent the damage resulting from the rapid inflow of the storm pressure caused by conventional weapons and nuclear explosions, thereby protecting the people and equipment in the shelter.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.49-51
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• 2004

This paper presents a realization of fault detection algorithm and Fieldbus based communication for condition monitoring of check valve. We first acquired the AE(Acoustic Emission) sensor data at the KAERI check valve test loop, extract fault features through the learned Neural network, and send the processed data to a remote site. The overall system has been implemented and experimental results are given to show its effectiveness.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.113-117
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• 1997

In this paper we detected the thrombosis formation by spectral analysis and neural network. Using microphone and amplifier, we measured the sound from the mechanical valve which is attached to the pneumatic ventricular assist device. The sound was sampled by A/D converter and the periodogram is the main algorithm or obtaining spectrum. We made the valvular thrombosis models using pellethane and silicon and they are thrombosis model on the disk, around the sewing ring and fibrous tissue growth across the orifice of valve. The spectrum of normal and 5 kinds of thrombotic valve were obtained and primary and secondary peak appeared in each spectrum waveform. So to distinguish the secondary peak of normal and thrombotic valve quantatively, 3 layer back propagation neural network.

• Journal of Magnetics
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• v.12 no.4
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• pp.152-155
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• 2007

Spin dependent transport was investigated in lateral $Py(Ni_{81}Fe_{19})/Au/Py$ spin valve devices. Clear spin valve effect was observed in conventional four-terminal measurement geometry. Higher resistance was found in antiparallel magnetization field of two Py electrodes which is determined by anisotropy magnetoresistance (AMR) measurements. The rectangular shape of spin signal together with good agreement of switching field convinces observed spin valve signal is resulted from effective spin injection and detection. The magnetoresistance ratio decays exponentially with channel length by which spin diffusion length of Au channel was estimated to be 76 nm.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.