• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.324-328
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• 2007

Vibration monitoring of rolling element bearings is probably the most established diagnostic technique for rotating machinery. Acoustic Emission (AE) Analysis is an extremely powerful technology that can be used within a wide range of applications of non destructive testing. Therefor, this paper investigates the detection methods using AE for rolling element bearings about low-speed. Two transducers, the accelerometer and acoustic emission sensor, are used to acquire data and the results are compared for the capacity of early fault detection.

• Journal of the korean Society of Automotive Engineers
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• v.14 no.5
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• pp.83-94
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• 1992

There has been a suggestion of many techniques as the methods of diagnosis for rotational machinery. In this study, HFRT was used as the analysis method for ball bearing of automobile and was compared with the conventional ANC technique. And this paper presented the computer simulation process about fault types and noise for the validity of the algorithm and identification of the physical meanings of HFRT. Also, experiment was performed using ball bearing and the results showed that HFRT was much more effective than the conventional methods in diagnostic process.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.1
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• pp.48-57
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• 1995

연삭가공중에 발생하는 트러블의 인식과 처리는 공학적 원리에 입각한 방법과 현장 숙련기술자의 경험적 지식을 바탕으로한 방법이있다. 그러나, 연삭가공은 관계되는 가공변수가 많아, 이들 상호간의 관계를 정량적으로 규명할 수 없어 대부분이 숙련자의 지식에 의존하는 것이 현실정이다. 본 논문은 이와같은 점에 착안하여 원통연삭을 대상으로 현장숙련자가 갖고 있는 경험적이고도 정성적인 지식의 호과적인 활용을 위해 계층분석으로 도입하여 이들이 갖고 있는 노하우를 정량화하고, 아울러 공학적원리를 가미한 연삭가공을 트러블 진단. 처리 시스템을 구축하였다. 또한 시스템 구성에 신뢰성을 높이기 위해 폴트 진단 모델을 도입하였다.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.7
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• pp.449-453
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• 2016

The amount of electrical energy has been increased with the rapid development of the industrial society. Accordingly, operating voltage of the power equipment and facility capacity are continuously increasing. Development trends of recent high-voltage electrical equipment are ultra high-voltage, large-capacity and compact. Early diagnosis of a failure of the power plant has been emerging as an important task as to supply high quality power to users. In this study, we have tried to develope an algorithm for distinguishing an arc fault signal generated in the power plant by using UV sensor.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.4
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• pp.134-140
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• 2016

Main Spindle System has effect on performance of machine tools and working quality as well as is required of high reliability. Especially, it takes great importance in producing automobiles which includes a large number of working processes. However, main spindle unit in Machine tools are often cases where damage occurs do not meet the design life due to driving in harsh environments. This is when excessive maintenance and repair of machine tools or for damage stability has resulted in huge economic losses. Therefore, this studying propose a method of accelerated life test for diagnosing and prognosis the state of life assessment main spindle system. Time status monitoring of diagnostic data - through the analysis of the frequency band signals were carried out inside the main spindle bearing condition monitoring and fault diagnosis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.932-937
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• 2003

When oil pressure of damper is lost or relative stiffness of spring drops in vibration system, it can be fatally dangerous situation. A fault diagnosis method for vibration system using Support Vector Machine(SVM)is suggested in the paper. SVM is used to classify input data or applied to function regression. System status can be classified by judging input data based on optimal separable hyperplane obtained using SVM which learns normal and abnormal status. It is learned from the relationship of system state variables in term of spring, mass and damper. Normal and abnormal status are learned using phase plane as in put space, then the learned SVM is used to construct algorithm to predict the system status quantitatively

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.643-646
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• 2005

The paper discusses the main problems in induction motor diagnosis by motor current and vibration signals, possible faults and effects produced by these faults in the signal spectrums. Decision Tree is introduced as a tool to diagnose the motor status, this expert system is implemented to detect the incipient defects, supervise and predict them, and plan the maintenance of the motor.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.120-124
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• 2002

A new methodology is presented to diagnose faults in equipment plasma. This is accomplished by using neural networks as a pattern recognizer of radio frequency (rf) impedance match data. Using a match monitor system, the match data were collected. The monitor system consisted mainly of a multifunction board and a signal flow diagram coded by Visual Designer. Plasma anomaly was effectively represented by electrical match positions. Twenty sets of fault-symptom patterns were experimentally simulated with variations in process factors, which include rf source power, pressure, Ar, and $O_$2 flow rates. As an input to neural networks, two means and standard deviations of positions were used as well as a reflected power. Diagnostic accuracy was measured as a function of training factors, which include the number of hidden neurons, the magnitude of initial weights, and two gradients of neuron activation functions. The accuracy was the most sensitive to the number of hidden neurons. Interaction effects on the accuracy were also examined by performing a 2$^$4 full factorial experiment. The experiments were performed on multipole inductively coupled plasma equipment.

• Transactions on Electrical and Electronic Materials
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• v.7 no.6
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• pp.324-329
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• 2006

As the average usage period of transformers increases, it is becoming increasingly necessary to know the internal condition of transformers. It is therefore critically important to establish monitoring and diagnostic techniques that can perform transformer condition assessment. Frequency response analysis, generally known as FRA, is one of the technologies to diagnose transformers. Using case studies, this paper presents the effectiveness of FRA as measurements for detecting transformer failures. This paper introduces the fact that FRA waveforms have useful information about diagnosis of failure on core earths and winding shield, and that the condition outside transformers can affect frequency response characteristics.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1903-1905
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• 2000

Power transformers have a tendency of ultra-high voltage capacity as power demand increases day after day KEPCO also will have plan to supply transmission power from 345KV to 765KV in the early of 2000. Therefore, the fault by insulation destruction gives rise to large area of power failure in huge capacity transformers. On-line predictive diagnostics is very important in power transformers because of economic loss and its spreading effect. This study presents the algorithm for transformer oil analysis used KEPCO code, IEC code, gas pattern method and Dornenburg & Roger Ratio method. We also describe the MMI display of expert system programmed by Element Expert Tool(Neuron Data Inc.).