• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2003년도 추계종합학술대회 논문집
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• pp.276-280
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• 2003

Somani와 Peleg은 k개의 부정확한 진단을 용인함으로써 결함의 개수가 t(차원)개를 초과할 경우에도 시스템을 진단하는 t/k-diagnosable 시스템을 제안하였다. 한편 Kranakis와 Pelc는 결함의 개수가 t개를 초과하지 않는 경우에 하이퍼큐브를 보다 효율적으로 진단하는 알고리즘을 제안하였다. 이 논문에서는 Somani 등이 제안한 것처럼 k=1, 2, 3개의 부정확한 진단을 용인하는 경우에 Kranakis 등이 제안한 효율적인 방법을 기반으로 하이퍼큐브를 진단하는 알고리즘을 제안한다. 그리고 제안한 알고리즘이 약 두 배 이상 더 많은 결함을 진단하면서도 기존의 알고리즘보다 효율이 거의 떨어지지 않는다는 사실을 분석을 통하여 확인할 수 있었다.

• 제어로봇시스템학회논문지
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• 제19권9호
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• pp.822-826
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• 2013

In this paper, we propose an AFDO (Adaptive Fault Diagnosis Observer) and a fault tolerant controller for a class of nonlinear continuous-time system under the nonlinear abrupt actuator faults. Together with its estimation laws, the AFDO which estimates that the actuator faults is designed by using the Lyapunov analysis. Then, based on the designed AFDO, an adaptive sliding mode controller is proposed as the fault tolerant controller. Using Lyapunov stability analysis, we also prove the uniform boundedness of the state, the output and the fault estimation errors, and the asymptotic stability of the tracking error under the nonlinear time-varying faults. Finally, we illustrate the effectiveness of the proposed diagnosis method and the control scheme thorough computer simulations.

• 한국자동차공학회논문집
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• 제15권6호
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• pp.9-16
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• 2007

This paper describes the design and implementation of the fault diagnosis on the Electronic Throttle Control(ETC) System. The proposed fault diagnosis consists of an input signal, actuator and a processor diagnosis. The input signal diagnosis can detect the faults of the ETC system's input signals such as the position sensor fault, source voltage fault, load current fault, and desired position fault. The actuator diagnosis is able to detect the actuator fault due to the actuator aging and an obstacle which interfere in the movement of the actuator. The processor diagnosis detects the fault which prevents the microprocessor from operating the ETC software. In order to protect the breakdown of the ETC system and assure the driving safety, appropriate reactions are also proposed according to the detected faults. The safety and reliability of the ETC system can be improved by the proposed fault diagnosis.

• 대한전자공학회논문지SD
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• 제43권9호
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• pp.59-63
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• 2006

VLSI의 복잡도가 증가함에 따라, 보다 복잡한 고장이 나타나게 되었다. 단일 고장 진단을 위한 많은 방법들이 연구되어 왔다. 때로는 오류가 존재하는 칩에 대한 다중 결함이 실제 현상을 보다 더 정확하게 반영한다. 따라서 다중 고착 고장을 위한 효율적인 고장 진단 알고리듬을 제한하겠다. 제안하는 매칭 알고리듬은 완전일치공통부분을 고장 진단의 중요한 기준으로 사용함으로써 단일 고착 고장 시뮬레이터 환경에서도 다중 고착 고장을 진단할 수 있다. 또한 각 고장간의 식별성을 높여 다중 고착 고장을 진단함에도 불구하고, 고장 후보의 수를 획기적으로 줄일 수 있었다. 이를 위하여 출력단의 수에 따른 가중치 개념과 가산, 감산 연산을 사용하였다. 제안한 매칭 알고리듬은 ISCAS85회로와 완전 주사 스캔이 삽입된 ISCAS89회로에서 실험하여 성능을 입증하였다.

• 한국정보통신학회논문지
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• 제15권1호
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• pp.241-248
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• 2011

본 논문에서는 핵연료 교환기 헤드를 제어하는 진단시스템을 설계하고 구현하였다. 제안하는 핵연료 교환기 진단시스템은 신호 수집 시스템, 진단 알고리즘, 고장 시뮬레이터의 세 부분으로 구성된다. 핵연료 교환기를 직접 사용하는 실험은 원전 운영상 불가능하여 본 연구에서는 고장 시뮬레이터로 베어링 이상 상태를 생성시키고 FFT 및 웨이블릿 변환을 이용하여 고장 진단 실험을 수행하였다. 베어링 볼 이상 상태 진동 분석과 베어링 내륜 이상 상태 진동 분석을 통해 이론값과 실험값이 거의 일치함을 확인하였다.

• 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.

• International Journal of Automotive Technology
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• 제6권4호
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• pp.341-350
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• 2005

In this study, fault diagnosis and monitoring of serial wound pre-engaged starter motors have been carried out. Starter motors are DC motors that enable internal combustion engine (ICE) to run. In case of breakdown of a starter motor, internal combustion engine can not be worked. Starter motors have vital importance on internal combustion engines. Kohonen network based fault diagnosis system is proposed for fault diagnosis and monitoring of starter motors. A graphical user interface (GUI) software has been developed by using Visual Basic 6.0 for fault diagnosis. Six faults, seen in starter motors, have been diagnosed successfully by using the developed fault diagnosis system. GUI software makes it possible to diagnose the faults in starter motors before they occur by keeping fault records of past occurrences.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.109-114
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• 1993

It is well known that neural networks can be used to diagnose multiple faults to some limited extent. In this work we present a Multiple Fault Diagnosis Method (MFDM) via neural network which can effectively diagnose multiple faults. To diagnose multiple fault, the proposed method finds the maximum value in the output nodes of the neural network and decreases the node value by changing the hidden node values. This method can find the other faults by computing again with the changed hidden node values. The effectiveness of this method is explored through a neural-network-based fault diagnosis case study of a fluidized catalytic cracking unit (FCCU).

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.895-902
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• 2007

Rotating machinery has two typical faults with clearance, one is partial rub and the other is looseness. Due to these faults, non-linear and non-stationary signals are occurred. Therefore, time-frequency analysis is necessary for exact fault diagnosis of rotating machinery. In this paper newly developed time-frequency analysis method, HHT(Hilbert-Huang Transform) is applied to fault diagnosis and compared with other method of FFT, SFFT and CWT. The results show that HHT can represent better resolution than any other method. Consequently, the faults of rotating machinery are diagnosed efficiently by using HHT.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1558-1565
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• 2015

In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in three-phase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.