• International Journal of Aeronautical and Space Sciences
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• 제9권2호
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• pp.64-70
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results. it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• 설비공학논문집
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• 제17권8호
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• pp.780-788
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• 2005

Two different types of algorithms were developed and applied to detect the partial faults of a multi-type air conditioning system. Partial faults include the compressor valve leakage, the refrigerant pipe partial blockage, the condenser fouling, and the evaporator fouling. The first algorithm was developed by using mathematical models and parity relations, and the second algorithm was developed by using mathematical models and a RBF neural network. Test results showed that the second algorithm was better than the first algorithm in detecting various partial faults of the system. Therefore, the algorithm developed by using mathematical models and a RBF neural network may be used for the detection of partial faults of an air-conditioning system.

• 대한전자공학회논문지SD
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• 제42권10호
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• pp.47-54
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• 2005

본 논문에서는 불완전명세(incompletely-specified)를 가진 상태전이그래프(state transition graph: STG)상에서 리던던트 고장(redundant faults)수를 줄여 테스트를 용이하게 하기 위한 새로운 동기 순차회로의 합성방법을 제안한다. 이 STG 합성법에는 1) 구별전이(distinguishable transition)을 이용하여 무정의상태(undefined states)와 불완전명세된 입력전이를 추가하고, 2) 가능한 한 강연결(strongly-connected)이 되도록 하는 방법을 사용한다. 제안된 방법을 이용하여 MCNC 벤치마크 회로에 대해 실험한 결과, 대부분의 회로에 대해 무해 고장의 수가 현격히 줄어들어 높은 고장검출을 얻었다.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.84-88
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results, it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• 제어로봇시스템학회논문지
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• 제17권11호
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• pp.1138-1144
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• 2011

This article deals with the problem of fault detection of a spacecraft's actuators. The authors introduce an extended unknown input observer for nonlinear systems. This is an extended form of unknown input observers which are used for linear systems. Since faults are not available, those are considered as unknown inputs. Unknown input observers can estimate states without full information of inputs if some conditions are satisfied. The authors suggest a continuous-time extended UIO (eUIO) and prove the convergence of state estimation errors. Since the dynamic equation of a spacecraft is nonlinear, an extended UIO can be applied. Three eUIOs are designed to monitor three reaction wheels. The moving averages of each eUIO's residuals are selected for decision logic. The proposed method is verified by numerical simulations.

• Transactions on Control, Automation and Systems Engineering
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• 제3권2호
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• pp.77-82
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• 2001

This paper deals with a fault detection algorithm for linear descriptor systems via unknown input PI observer. An unknown input PI observer is presented and its realization conditions is proposed by using the rank condition of system matrices. From the characteristics of unknown input PI observer, the states of system with unknown inputs are estimated and the occurrences of fault are detected, and its magnitudes are estimated easily by using integrated output estimation error under the step faults. Finally, a numerical example is given to verify the effectiveness of the proposed fault detection algorithm.

• 전기학회논문지
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• 제66권10호
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• pp.1445-1453
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• 2017

In this paper, we propose a fault detection model based on multi-layer neural network using data mining technique for faults due to boiler tube leakage in a thermal power plant. Major measurement data related to faults are analyzed using statistical methods. Based on the analysis results, the number of input data of the proposed fault detection model is simplified. Then, each input data is clustering with normal data and fault data by applying K-Means algorithm, which is one of the data mining techniques. fault data were trained by the neural network and tested fault detection for boiler tube leakage fault.

• 대한전자공학회논문지
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• 제12권4호
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• pp.21-27
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• 1975

본 논문에서는 분기가 있는 일반조합논리회로의 다중결함을 검출할 수 있는 테스트집합을 구하는 절차를 유도하였다. 일반논리회로를 우선 내부분기점을 전후하여 이를 분기가 없는 부분회로로 분리하고 각 부분회로에 대한 최소테스트집합을 구한다. 다음에 각 부분테스트를 최대한으로 병립시켜 합성테스트를 구하여 종합적인 일차입력벡터를 정한다. 이러날 수 있는 모든 결함을 빠짐없이 피복할 수 있는 최소테스트집합을 구해가는 과정에 대해서는 각 를 들어 상세히 설명하였다. In this paper, a procedure for deriving of multiple fault detection test sets is presented for fan-out reconvergent combinational logic networks. A fan-out network is decomposed into a set of fan-out free subnetworks by breaking the internal fan-out points, and the minimal detecting test sets for each subnetwork are found separately. And then, the compatible tests amonng each test set are combined maximally into composite tests to generate primary input binary vectors. The technique for generating minimal test experiments which cover all the possible faults is illustrated in detail by examples.

• 조명전기설비학회논문지
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• 제17권5호
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• pp.43-50
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• 2003

본 논문은 신경회로망을 이용하여 변압기 사고검출 기법을 제안하였다. 계전기 정동작을 위하여 전력용 변압기의 외부사고와 돌입현상은 포화현상이 고려된 EMTP/ATP를 이용하였고, 내부사고는 EMTP/BCTRAN를 이용하여 얻은 전류 데이타를 신경회로망의 사고검출 성능으로 평가하였다. 신경회로망의 입력지수로는 변압기 양단전류를 FFT로 주파수 분석하여 얻은 억제전류와 동작전류의 고조파 비의 크기를 이용하였고, 외부사고 시 억제전류값이 크게 나타나는 것을 이용하기 위해 억제전류를 동작전류로 나눈값을 계전기 입력으로 사용하였고, 학습알고리즘은back-propagation을 사용하였다. 실 계통에 적용하고 있는 변압기 보호용 계전기의 특성을 신경회로망의 검출성능으로 테스트한 결과 제안된 기법이 뛰어남이 확인되었다.

• 한국산학기술학회논문지
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• 제17권7호
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• pp.492-498
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• 2016

운전 중인 유도전동기에 고장이 발생하면, 구동장치 등 전체 시스템에 2차적인 고장을 유발 시킬 수 있다. 이 경우 구동시스템의 신뢰도와 안전성이 저하되고, 경제적인 손실을 초래할 뿐만 아니라, 인명 피해의 위험 등 많은 문제가 발생할 수 있다. 따라서 유도전동기의 고장징후를 조기 감지하여 전체 시스템 고장을 방지할 수 있도록 하는 유도전동기 고장진단 방법이 필요하다. 본 논문은 유도전동기에서 고정자권선의 부분 단락과 회전자 바의 균열이 발생하는 경우, 인버터 입력전류를 분석하여 고장징후를 조기 감지하는 유도전동기 고장진단 방법을 제안한다. 제안한 고장진단 방법은 고정자 전류 3개를 모두 센싱해야 하는 기존 고장진단 방법과 달리, 인버터 입력전류 센서 한 개만으로 유도전동기 고장진단이 가능하다. 또한, 정상전류 주파수성분과 고장전류 주파수성분이 서로 분리되어 나타나는 인버터 입력전류 특성을 통해 기존 고장진단 방법보다 비교적 쉽고 확실한 고장진단이 가능하다. 시뮬레이션을 통하여 제안한 유도전동기 고장진단 방법의 우수성과 유효성을 확인한다.