• 전기학회논문지
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• 제57권3호
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• pp.398-402
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• 2008

Recently, the development of superconducting fault current limiters (SFCLs) has been required as power demands increase in the power system. A distribution-level prototype resistive SFCL using coated conductor (CC) has been developed by Hyundai Heavy Industries Co., Ltd. and Yonsei University for the first time in the world. The ratings of the SFCL are 13.2kV/630A at normal operating condition. A novel non-inductive winding method is used in fabricating coils so there is almost zero impedance during normal operation. The distribution SFCL is cooled by sub-cooled liquid nitrogen $(LN_2)$ of 65K and 3 bar to enhance cryo-dielectric performance, critical current density, and thermal conductivity. In order to make reliable operation of an SFCL in real power systems, we monitored and controled its operation conditions by using supervisory control and data acquisition (SCADA) method. Thus, a monitoring system for the SFCL employing information technology (IT) is proposed and developed to be on the lookout for the operation conditions such as inside temperature, inside pressure, $LN_2$ level, voltage and current. Since operation temperature should be kept constant, bang-bang control for temperature feedback with a heater attached to the cold head of cryo-cooler is applied to the system. Short-circuit tests with prospective fault current of 10kA and AC dielectric withstand voltage tests up to 143kV for 1 minute were successfully performed at Korea Electrotechnology Research Institute. This paper deals with the development of a distribution level SFCL and its monitoring system for reliable operation.

• 한국안전학회지
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• 제19권1호
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• 제어로봇시스템학회논문지
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• 제11권8호
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• pp.662-668
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• 2005

This paper presents a realization of check valve condition monitoring system based on fault diagnosis algorithm and Fieldbus communication. We first acquired AE(acoustic emission) sensor data at the check valve test loop, extract fault features through the teamed neural network, and send the processed data to a remote site. The overall system has been implemented and experimented results are given to show its effectiveness.

• Journal of Electrical Engineering and Technology
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• 제6권4호
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• pp.563-572
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• 2011

Reliability is an essential factor in a navigation system. Therefore, an integrity monitoring system is considered one of the most important parts in an avionic navigation system. A fault due to systematic malfunctioning definitely requires integrity reinforcement through systematic analysis. In this paper, we propose a method to detect faults of the GPS signal by using a distributed nonlinear filter based probability test. In order to detect faults, consistency is examined through a likelihood ratio between the main and auxiliary particle filters (PFs). Specifically, the main PF which includes all the measurements and the auxiliary PFs which only do partial measurements are used in the process of consistency testing. Through GPS measurement and the application of the autonomous integrity monitoring system, the current study illustrates the performance of the proposed fault detection algorithm.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.670-675
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• 1994

Yhis paper describes a fault diagnosis simulation of the Real-Time Multiple Fault Dignosis System (RTMFDS) for forcasting faults in a system and deciding current machine state from signal information. Comparing with other diagnosis system for single fault,the system developed deals with multiple fault diagnosis,comprising two main parts. One is a remotesignal generating and transimission terminal and the other is a host system for fault diagnosis. Signal generator generate the random fault signal and the image information, and send this information to host. Host consists of various modules and agents such as Signal Processing Module(SPM) for sinal preprocessing, Performence Monotoring Module(PMM) for subsystem performance monitoring, Trigger Module(TM) for multi-triggering subsystem fault diagnosis, Subsystem Fault Diagnosis Agent(SFDA) for receiving trigger signal, formulating subsystem fault D\ulcornerB and initiating diagnosis, Fault Diagnosis Module(FDM) for simulating component fault with Hierarchical Artificial Neural Network (HANN), numerical models and Hofield network,Result Agent(RA) for receiving simulation result and sending to Treatment solver and Graphic Agent(GA). Each agent represents a separate process in UNIX operating system, information exchange and cooperation between agents was doen by IPC(Inter Process Communication : message queue, semaphore, signal, pipe). Numerical models are used to deseribe structure, function and behavior of total system, subsystems and their components. Hierarchical data structure for diagnosing the fault system is implemented by HANN. Signal generation and transmittion was performed on PC. As a host, SUN workstation with X-Windows(Motif)is used for graphic representation.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.15-19
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• 1994

The application of neural network for fault dection of cutting force in turning was introduced. This monitoring system consist of a RBF predicton model and a ART-1 pattern classifier. RBF prediction model predict a cutting force signal. Prediction error of predictor is used for a input vector of ART-1 pattern classifier. Prediction error could be successfully performed to fault signal monitoring of ART-1 pattern classifier.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 추계학술대회 논문집 전기물성,응용부문
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• pp.112-116
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• 2004

On-line monitoring of fault discharge is an important approach for indicating the condition of electrical insulation of stator winding in high voltage motor. In this paper, several key aspects of on-line monitoring system are discussed, involving the characteristics of fault discharge of stator winding in high voltage motor, spectrum analysis of four simulation fault signals, feature extraction of internal fault discharge from apply voltage to breakdown. The study of the partial discharge activities allows to highlight the ageing stage in the winding fault under test. During the life of the winding insulation fault, the shape of PD signal change relating to the ageing stage. The ageing of stator winding insulation fault of high voltage motor is investigated based on the characteristics of partial discharge pulse distribution and statistical parameters, such as maximum, skewness and kurtosis using discrete wavelet transform coefficients.

• International Journal of Reliability and Applications
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• 제2권1호
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• pp.57-71
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• 2001

Global competition to increase production output and to improve quality is spurring manufacturing companies to use condition monitoring and fault diagnostic systems for predictive maintenance. As monitoring, testing, and measuring techniques develop, predictive control of components and complete systems have become more practical and affordable. In this article, we will consider the computer based data acquisition system for condition monitoring and the condition parameter analysis techniques for fault detection and diagnostics in the machinery and briefly discuss reliability prediction and the limit value determination in condition monitoring.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.978-983
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• 2003

In this paper, we propose a fault detection method for extended Kalman filter in decentralized filter structure. To detect a fault, a consistency between filter output and a monitoring signal is tested. State propagators are used to obtain the monitoring signal. However, the output of state propagator increases in magnitude and finally diverges as time runs. To solve such problem, two-propagator method was proposed for linear system. Two propagators are reset by Kalman filter output, alternatively, to avoid divergence. But a test statistics change abruptly at the reset instant in that method. Hence a N-step propagator method is proposed to fix up the problem. In the N-step propagator, only time propagations are performed from k-N+1 step to k step without measurement updates. A test statistics are defined by errors and its covariance between extended Kalman filter and N-step propagator. These fault detection methods are applied to integrated strapdown inertial navigation system (SDINS). By computer simulation, it is shown that the proposed methods detect a fault effectively.