• 한국전자파학회논문지
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• 제14권10호
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• pp.1079-1088
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• 2003

본 논문에서는 도선상의 결함 감지 및 추정 방법인 고분해능 시간-주파수 반사측정기법(time-frequency domain reflectometry)을 제안한다. 이 방법은 관측된 신호를 시간과 주파수 영역에서 동시에 분석 가능한 시간-주파수 상호 상관 관계(time-frequency cross correlation) 특성을 채택하였다. 제안된 방법인 TFDR의 정확도는 고주파용 coaxial cable을 가지고 실험을 통하여 기존의 방식들과 비교 검증하였다. 제안된 알고리즘의 다양한 결함들에 대한 감지 및 추정 결과, 기존의 방법에 비해 월등하다는 것을 명백히 나타내었다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 추계학술대회 논문집
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• pp.338-345
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• 2013

Induction motors play an increasing importance in industrial manufacturing. Therefore, the state monitoring systems also have been considering as the key in dealing with their negative effect by absorbing faulty symptoms in motors. There are numerous proposed systems in literature, in which, several kinds of signals are utilized as the input. To solve the multiple faults problem of induction motors, like the proposed system, the vibration signals is good candidate. In this study, a new signal processing scheme was utilized, which transforms the time domain vibration signal into the spatial domain as an image. Then the spatial features of converted image then have been extracted by applying the dominant neighbourhood structure (DNS) algorithm. In addition, these feature vectors were evaluated to obtain the fruitful dimensions, which support to discriminate between states of motors. Because of reliability, the conventional one-against-all (OAA) multi-class support vector machines (MCSVM) have been utilized in the proposed system as classifier module. Even though examined in severity levels of signal-to-noise ratio (SNR), up to 15dB, the proposed system still reliable in term of two criteria: true positive (TF) and false positive (FP). Furthermore, it also offers better performance than five state-of-the-art systems.

• 대한전기학회논문지:전력기술부문A
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• 제48권10호
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• pp.1272-1278
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• 1999

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to fault diagnosis. In this paper, we proposes an FDI system for nonlinear systems using neuro-fuzzy inference system. The proposed diagnostic system consists of two neuro-fuzzy inference systems which operate in two different modes (parallel and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis Function) network to identify the faults. The proposed FDI scheme has been tested by simulation on two-tank system.

• 한국추진공학회지
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• 제15권2호
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• pp.1-7
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• 2011

다양한 비행환경에서 장시간 체공하며 운용되는 UAV에서 추진시스템을 신뢰성 있게 운용하는 것은 매우 중요하다. 이런 UAV에 사용되는 터보프롭 엔진의 정확한 손상진단은 신뢰성과 이용률을 향상시킬 수 있다. 본 연구에서는 엔진 측정 파라미터들의 변화로부터 퍼지 이론을 적용하여 손상된 구성품을 식별한 후 훈련된 신경망 알고리즘을 식별된 손상 패턴에 적용하여 손상된 양을 정확히 진단할 수있는 방법을 제안하였다. 이렇게 제안된 진단 방법은 단일손상과 다중손상 모두 진단할 수 있다.

• Journal of Electrical Engineering and Technology
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• 제8권2호
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• pp.288-294
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• 2013

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2010년도 제35회 추계학술대회논문집
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• pp.499-505
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• 2010

다양한 비행환경에서 장시간 체공하며 운용되는 UAV에서 추진시스템을 신뢰성 있게 운용하는 것은 매우 중요하다. 이런 UAV에 사용되는 터보프롭 엔진의 정확한 손상진단은 신뢰성과 이용률을 향상시킬 수 있다. 본 연구에서는 엔진 측정 파라미터들의 변화로부터 퍼지 이론을 적용하여 손상된 구성품을 식별한 후 훈련된 신경망 알고리즘을 식별된 손상 패턴에 적용하여 손상된 양을 정확히 진단할 수 있는 방법을 제안하였다. 이렇게 제안된 진단 방법은 단일손상과 다중손상 모두 진단할 수 있다.

• 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).

• 한국전자통신학회논문지
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• 제5권3호
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• pp.315-320
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• 2010

다중 클래스로 이루어진 패턴을 분류하는 것은 데이터 베이스 시스템에서 기록을 그룹화하거나 VLSI 회로에서 어디에 결함이 있는지를 찾는 것 등에서 중요한 역할을 한다. 본 논문에서는 주어진 다중 클래스 패턴을 MACA(Multiple Attractor Cellular Automata)와 부분공간의 개념을 이용하여 가능한 최소 메모리량을 필요로 하는 다중 클래스 패턴 분류기를 구성하는 알고리즘을 제안한다.

• 전기학회논문지
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• 제57권4호
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• pp.582-588
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• 2008

Microgrids are new distribution level power networks that consist of various electronically-interlaced generators and sensitive loads. The important control object of Microgrids is to supply reliable and high-quality power even during the faults or loss of mains(islanding) cases. This paper presents power control methods to coordinate multiple distributed generators(DGs) against abnormal cases such as islanding and load power variations. Using speed-droop and voltage-droop characteristics, multiple distributed generators can share the load power based on locally measured signals without any communications between them. This paper adopts the droop controllers for multiple DG control and improved them by considering the generation speed of distribution level generators. Dynamic response of the proposed control scheme has been investigated under severe operation cases such as islanding and abrupt load changes through PSCAD/EMTDC simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1727-1731
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• 2003

In this paper we proposed backward fuzzy Petri-net to diagnoses faults in power systems by using the time sequence information of protection system. As the complexity of power systems increases, especially in the case of multiple faults or incorrect operation of protective devices, fault diagnosis requires new and systematic methods to the reasoning process, which improves both its accuracy and its efficiency. The fuzzy Petri-net models of protection system are composed of the operating process of protective devices and the fault diagnosis process. Fault diagnosis model, which makes use of the nature of fuzzy Petri-net, is developed to overcome the drawbacks of methods that depend on operator knowledge. The proposed method can reduce processing time and increase accuracy when compared with the traditional methods. And also this method covers online processing of real-time data from SCADA (Supervisory Control and Data Acquisition)