• The Transactions of the Korean Institute of Electrical Engineers C
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• v.48 no.2
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• pp.127-132
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• 1999

Both motion of free metallic particles from which most GIS internal failures are caused and partial discharges from the particles were examined using EHV GIS test chamber. Effects from particle length and gas pressure which are main factors to lead to breakdown failure were investigated theoretically and experimentally. Magnitude of both lift-off voltage and partial discharge inception voltage were measured respectively and, through these measurements, this paper showed the possibility of predicting breakdown fault and of taking action to prevent the fault in advance. The measurement of partial discharge when the particles began to move could be adopted to decide minimum sensitivity in developing predictive diagnostic equipments. Both the amount of apparent discharge and real discharge in GIS were examined theoretically and experimentally, then experimental results were analyzed on the basis of the theory.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.147-154
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• 1998

As technical systems in building are being developed, the processes and systems get more difficult for the average operator to understand. When operating a complex facility, it is beneficial in equipment management to provide the operator with tools which can help in dicision making for recovery from a failure of the system. The main object of the study is to develop real-time automatic fault detection and diagnosis system for optimal operation of IBS building.

• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1676-1678
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• 2001

In general, power demand is on an increasing trend as industries have made rapid strides. Power transformer is the most important equipment in substation for this reason. Transformer trobles go with blackout, expensive repair costs and huge economic losses. Therefore it is important to find the quick detection of incipient fault for the least losses. There have been gas, partial discharge, temperature, OLTC, fan and pump diagnosis for preventive techniques by present. Specially gas analysis has been adapted for a long time and proved as confident method. In this paper, we analysed the fault causes of used power transformer. The insulation faults was occupied 40% of inquired 152 faults from 1991 to 2000. This study presents the developed algorithm and expert system for finding abnormal status within transformer. We used the Element Expert tool developed Neuron DATA Inc.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.98.3-98
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• 2001

The research for detecting of insulating deterioration in transformer has been studied from long ago. Analysis method of combustible gas, which is included in insulating oil, has been widely used in detection of transformer pre-fault detection due to the effectiveness of its method. Recently the fault effect of the large transformer is very critical in a power system, therefore the on-line monitoring and diagnostic system is needed. In addition, the more accurate method of detecting a Partial Discharge (PD) location should be developed. For preventive maintenance against discharge failures, it is important not only to detect the discharges, but also to accurately estimate their positions. However ...

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.325-329
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• 1992

The development of automatic production system have required intelligent diagnostic and monitoring function to repair system failure and reduce production loss by the failure. In order to perform accurate functions of intelligent system, inference about total system failure and fault analysis due to each mechanical component failures are required. Also the solution about repair and maintenance can be suggested from these analysis results. Generally, bearing is a essential mechanical component in the machinery. The bearing failure is caused by lubricant system failure, metallurgical defficiency, mechanical condition(vibration overloading misalignment), environmental effect. This study described roller bearing fault train due to stress variation and metallurgical defficiency from lubricant failure by using FTA.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.233-238
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• 2000

CNC machine tool is assembled by central processor, PLC(Programmable Logic Controller), and actuator. The sequential control of machine generally controlled by a PLC. The main fault occured at PLC in 3 control parts. In LC faults, operational fault is charged over 70%. This paper describes diagnosis model and data processing for remote monitoring and diagnosis system in machine tools with open architecture controller. Two diagnostic models based on the ladder diagram. Logical Diagnosis Model(LDM), Sequential Diagnosis Model(SDM), are proposed. Data processing structure is proposed ST(Structured Text) based on IEC1131-3. The faults from CNC are received message form open architecture controller and faults from PLC are gathered by sequential data.. To do this, CNC and PLC's logical and sequential data is constructed database.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.11
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• pp.1091-1100
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• 2004

This paper presents an approach for the monitoring and detection of faults in induction machine by using data fusion technique and Dempster-Shafer theory Features are extracted from motor stator current and vibration signals. Neural network is trained and Hosted by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electric and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real time application.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.6
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• pp.619-623
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• 2008

As higher reliability and accuracy on production facilities are required to detect incipient faults, a diagnostic system for predictive maintenance of the facility is highly recommended. In this paper, it presents a study on the application of vibration signals to diagnose faults for a rotating machinery using the Mahalanobis distance-Taguchi system. RMS, crest factor and Kurtosis that is known as the statistical methods and the spectrum analysis are used to diagnose faults as parameters of Mahalanobis distance.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.20-27
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• 2000

A chatter vibration and a workpiece burn are the main phenomena to be monitored in modern grinding processes. This study describes a trouble diagnosis of the cylindrical plunge grinding process using the power and acoustic emission (AE) signals. The raw signals of the power and the AE occurred during the grinding operation were sampled and analyzed to determine the relationship between each fault and change of signals. A neural network that has a high success rate of the fault detection was used. Furthermore, an analysis on the influence of parameters to the chatter vibration and the grinding burn was conducted.

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.19-21
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system(motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses the motor's condition. The diagnostic system is constructed by using LabVIEW of National Instruments.