• Proceedings of the KSR Conference
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• 1999.05a
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• pp.143-150
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• 1999

The vibration signals of driving parts of electric train are distorted its signal patterns due to the impact components, which occurs when wheel passes rail joints. An elimination method of the impact components is investigated using adaptive signal processing technique in this study. The result shows that adaptive interference canceling method seems to be more effective than line enhancement technique. The application of adaptive interference canceling method to the signal measured at bogie shows that the extractions of the signals of driving parts of traction motor, reduction gear, and axle bearing are successful. Therefore, only the signals of bogie, which is the place to attach an accelerometer easily, is sufficient for the fault diagnosis and the safety evaluation of electric train. Also, adaptive interference canceling method can be applicable to evaluate the performance of vibration isolation between bogie and car body and to investigate the characteristics of indoor sound.

• Proceedings of the KSR Conference
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• 2009.05b
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• pp.403-406
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• 2009

Recently, electricity is essential for human lives. Modem people take cultural benefits due to the development of the electric power system and the spread of the high tech-electric appliances, the cell phones, and etc. However, the electromagnetic field problems become prominent figures owing to the fault of the communication devices around the power line and the biological effect, and etc. In this paper, we introduce the simple electromagnetic field calculation based on the transmission line matrix method, prior to the analysis about the influence of electromagnetic field. Simulation object is the inner part of the trailer car in the high speed train. Unlike the existing paper we submitted, we analyze not only the magnetic field but the electric field in the inner part of the trailer car which makes up the high speed train.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.1
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.11
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• pp.589-595
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• 2004

This paper presents a modeling and simulation of a grid-connected wind turbine generation system with respect to wind variations, starting of large induction motor and three-phase fault in the system, and investigates voltage variations of the system for disturbances. It describes the modeling of the wind turbine system including the drive train model, induction generator model, and grid-interface model on MATLAB/Simulink. The simulation results show the variation of the generator torque, the generator rotor speed, the pitch angle, terminal voltage, system voltage, fault current, and real/reactive power output, etc. Case studies demonstrate that the pitch angle control is carried out to achieve maximum power extraction for wind speed variations, starting of a large induction motor causes a voltage sag due to a large starting current, and a fault on the system influences on the output of the wind turbine generator.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.17-20
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• 2004

This paper presents a modeling and simulation of a grid-connected wind turbine generation system with respect to wind variations and three-phase fault in the system. It describes the modeling of the wind turbine system including the drive train model, induction generator model, and grid-interface model on MATLAB/Simulink. Case studies demonstrate that the pitch angle control is carried out to achieve maximum power extraction for wind speed variations and the duration of a fault on the system influences on the output of the wind turbine generator.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.7
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• pp.679-686
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• 2013

The reduction unit is one of the most important components in railway cars, due to the transmission of torque from the motor to the wheels. Faulty reduction gears in high-speed trains result from excessive wear on the gear or damage to the gear. These types of gear defects have a significant effect on high-speed rail operation and safety; thus, a diagnosis system for the reduction unit is needed. Vibration diagnosis technology is one of the most effective diagnostics. In this paper, the vibration parameters of a reduction unit were evaluated during a driving-gear test and a full-vehicle test, using kurtosis and the crest factor. These tests were performed under normal operating conditions; a specimen tester was used to diagnose problems in defective gears.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.903-912
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• 2007

Faults of rotating parts of a train normally generate unexpected frequency band or impulsive sound[1] which has a period when it moves with a constant speed. The former can be detected by the moving frame acoustic holography method, which visualizes sound field that is generated by a moving and emitting pure tone or band limited noise source. We have attempted to apply the method to the latter case: the periodic impulsive sound which generate different signal compared with what can be measured by the band limited noise. The signal to noise ratio which determines the success of early fault detection must also be studied with the impulsive and moving signal. This research shows how the problems related with these issues can be resolved. The main idea is that periodic impulsive signal can be expressed by infinite set of discrete pure tones. This enables us to obtain lots of holograms that visualize periodic impulsive sound field including noise by using the moving frame acoustic holography method. Therefore holograms can be averaged to improve the signal to noise ratio until having reliable information that exhibits where the impulsive sources are. Theory and experiment by using the miniature vehicle are described [Work supported by BK21 & KRRI].

• Smart Structures and Systems
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• v.13 no.3
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• pp.453-471
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• 2014

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.63-68
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• 2017

Although automated metro subway systems have the advantage of operating a train without a train driver, it is difficult to detect an immediate fault condition and take countermeasures when an unusual situation occurs. Therefore, it is important to construct a maintenance information system (MIS) that detects the vehicle failure/status information in real time and maintains it efficiently in the depot of the railway's vehicles. This paper proposes a conceptual design method that realizes the interface between the train control system (TCS), the operation control center train control monitoring system (OCC-TCMS) console, and the MIS using wireless communication network in real-time. To transmit a large amount of information on 800,000 occurrences per day during operation, data was collected in a 56 byte data table using a data processing algorithm. This state information was classified into 4 hexadecimal codes and transmitted to the MIS by mapping the status and the fault information on the vehicle during the main line operation. Furthermore, the transmission and reception data were examined in real time between the TCS and MIS, and the implementation of the failure information screen was then displayed.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1335-1337
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• 2003

한국형 고속 전철 과제는 6년의 과제 기간을 가시는 국가 연구 사업으로, 한국 실정에 적합한 차세대 고속 전철을 시험 제작하여 운용하는 것이 목적이다. 시속 350 km/h의 운행 속도를 목표로 하는 한국형 고속 전철은 현재 개발이 완료되어, 시험 주행 트랙에서 증속을 위한 시험 운행을 계속하고 있다. 한국형 고속 전철은 열차 내 각종 제어 장치들 간의 데이터 교환를 위해서 실시간 네트워크인 열차통신 네트워크(Train Communication Network; TCN)를 사용한다. 약 10년간의 표준 보완 기간을 거쳐서 1999년 국제 표준으로 확정된 TCN(IEC61373)은 열차 전용의 실시간 통신 네트워크로 열차 장치의 제어 및 진단에 적합한 다양한 기능과 특징을 가지고 있다. 한국형 고속전철은 열차의 주 제어 및 감시를 담당하는 주관 제어장치(SCU, Supervisory Control Unit)와 열차 안전에 중요한 역할을 하는 자동 열차 제어 장치(ATC, Automatic Train Control)을 포함하는 55개의 제어 장치들이 TCN으로 연결되어서 상호간의 데이터 교환을 수행하도록 구성되어 있다. 본 논문에서는 한국형 고속전철에 사용될 TCN의 구조와 실제 필드에 사용되어지기 위해서 필수적으로 필요한 네트워크의 고장 진단 기법에 대해서 설명한다.