• Journal of the Korean Institute of Gas
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• v.11 no.1 s.34
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• pp.23-28
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• 2007

This paper presents an experimental investigation on the sealing defects and cracks of O-rings and a valve packing of a gas valve for a LPG cylinder. O-ring in which stops a gas leakage of a liquefied petroleum gas is very important for a LPG valve safety. Valve packing is to open and close a gas flow port for supplying and charging a LPG fuel. The sealing performance of two sealing units ism related to the leak safety and long lift of a gas valve. The investigated results show that most of O-rings was failed due to a circumferential crack in which is caused by partial press bonding failure near the partition zone and an excess compression rate. Some of the O-ring failure was originated by an extrusion of an excessive leak pressure of a LP gas. Thus, this paper strongly recommends a tight quality control and a safety guarantee system of O-rings and valve packing to guarantee a leak safety and to extend a service lift of a gas valve. At the end, a warranty policy of the sealing units should be adopted for increasing a product quality and safety of a gas valve.

• Journal of Drive and Control
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• v.6 no.3
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• pp.28-34
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• 2009

• The KSFM Journal of Fluid Machinery
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• v.5 no.4 s.17
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• pp.103-110
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• 2002

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2008.11a
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• pp.229-230
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• 2008

• Proceedings of the Korean Vacuum Society Conference
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• 2003.08a
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• pp.59-59
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• 2003

• Proceedings of the Korean Nuclear Society Conference
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• 2002.10a
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• pp.93-93
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• 2002

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.594-607
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• 2004

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.677-685
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• 2004

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.5
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• pp.601-607
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• 2013

This paper presents the development and validation of a wheel loader simulation model. The objective of doing so is to evaluate the performance of the wheel loader and improve its overall performance using Matlab/Simulink. The wheel loader simulation model consists of 4 parts: mechanical/hydraulic powertrain model and vehicle/working dynamic model. An integrated simulation model is required to evaluate and improve the performance of the wheel loader. It is expected that this model will be applied to fuel economizing, improving the pace of operation by using the hybrid system, and the intelligent wheel loader. The performance of the proposed simulation model has been validated by using Matlab/Simulink to compare the driving and the working experimental data.

• 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.