• 한국정밀공학회지
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• 제18권10호
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• pp.33-44
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• 2001

• 한국콘텐츠학회논문지
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• 제12권8호
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• pp.77-86
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• 2012

항공전자 시스템은 중요한 임무를 수행하는 다양한 전자 장치들로 구성되어지며 항공기의 성능을 위해 전자 장치의 수가 점차적으로 증가되고 있다. 이에 따라 개발 비용, 유지보수 비용, 운영비용 등이 증가되었고, 이를 해결하기 위해 항공전자 시스템의 기술 패러다임은 시스템을 독립적으로 관리하는 연방형 항공전자(Federated Avionics) 시스템에서 모듈 통합형 항공전자(IMA: Integrated Modular Avionics) 시스템으로 발전하고 있다. 모듈 통합형 항공전자 시스템은 연방형 시스템과는 달리 항공기의 각 기능들을 IMA 시스템에서 통합 처리하기 때문에 fault 발생 시 시스템 운용에 큰 영향을 미치게 되므로 고장 허용 기술이 필수 사항으로 적용되고 있다. 본 논문에서는 ARINC 653 기반의 모듈 통합형 항공전자 시스템에서 발생할 수 있는 fault를 정의하고 fault 발생 시 시스템이 지속적으로 정상 동작할 수 있도록 고장 허용 기법 설계 및 ARINC 653 표준에 따른 HM(Health Monitoring) 시스템 설계 방법을 제안한다.

• 조명전기설비학회논문지
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• 제27권3호
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• pp.65-78
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• 2013

To analyze influences under open fault conditions in switching devices, an integrated simulation and condition monitoring scheme for a permanent magnet synchronous generator (PMSG) based variable speed grid-connected wind turbine system are presented. Among various faults in power electronics components, the open fault in switching devices may arise when the switches are destructed by an accidental over current, or a fuse for short protection is blown out. Under such a faulty condition, the grid-side inverter as well as the generator-side converter does not operate normally, producing an increase of current harmonics, and a reduction in output and efficiency. As an effective way for a condition monitoring of generation system by online basis without requiring any diagnostic apparatus, the estimation schemes for generated voltage, flux linkage, and stator resistance are proposed and the validity of the proposed scheme is proved through comparative simulations.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권9호
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• 한국컴퓨터정보학회논문지
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• 제10권6호
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• pp.37-46
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• 2005

분산 환경 기반으로 많은 작업이 이뤄지는 상황에서 해당 자원에 대한 신뢰성 제공은 매우 중요하다. 자원 제공자는 서비스의 신뢰성을 제공하기위해 일반적으로 채택되는 유형은 서버 리플리케이션 구조 이다. 해당 서버에는 중복된 객체인 리플리카가 유지하는 구조로 이루어진다. 이러한 리플리케이션 구조에서는 안정된 리플리카의 서비스가 중요하므로, 각 리플리카의 결함 발생을 진단하기 위한 구조가 요구된다. 본 논문에서는 분산 객체 시스템에서 제공하는 리플리케이션 관리 시스템의 풀 모니터링 스타일에서 나타날 수 있는 단점을 극복한 CORBA의 푸쉬 모니터링 스타일 기반 효과적 푸쉬 결함 관리 알고리즘을 제안한다. 제안된 푸쉬 결함 모니터링 스타일의 실험결과 비교 대상 시스템에 비해 장애 검출과 관련한 작업량과 평균 타임아웃 비율 측면에서 가치 있는 결과를 확인 할 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제10권6호
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• pp.2315-2325
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• 2015

Induction motors are widely used in industrial processes since they offer a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to an unexpected interruption at the industrial plant. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the no-loaded case. To overcome this drawback, this paper deals with an efficient and new method to diagnose the induction-motor rotor fault based on the digital implementation of the monitoring algorithm based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform. Experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the combined “Time Synchronous Averaging – Discrete Wavelet Transform” approach.

• 제어로봇시스템학회논문지
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• 제6권12호
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2002년도 추계학술대회 논문집(I)
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• pp.497-502
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• 2002

TGIS(Train General Information System) is monitoring Train status and analyzing fault happened. TGIS informed a lot of Passenger data, Driving data, Status data and Fault data to 'Depot'. For dealing them more properly, TGIS need specified data and specialized data handling method. This paper suggest Optimize data of Train Monitoring System and Apply that to Main Line Car.

• 한국정밀공학회지
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• 제11권5호
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• pp.110-123
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• 1994

The development of automatic production systems have required inteligent diagnostic and monitoring function to repair system failure and reduce production loss by the failure. In order to perform accurate functions of intelligent system, inferencing about total system failure and fault analysis due to each mechanical component failures are required. Also the solution about repair and maintenance can be suggested from these analysis results. As an essential component of mechanical system, a bearing system is investigated to define the failure behavior. The bearing failure is caused by lubricant system failure, metallurgical defficiency, mechanical condition(vibration, overloading, misalignment) and environmental effect. This study described roller bearing fault train due to stress variation and metallurgical defficiency from lubricant failure by using FTA.

• 한국정밀공학회지
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• 제15권1호
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• pp.19-25
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• 1998

Remote fault diagnosis and maintenance system using general telecommunication network is necessary for an effective fault diagnosis and higher productivity of NC machine tools. In order to monitor machine tool condition and diagnose alarm states due to electrical and mechanical faults, a remote data communication system for monitoring of NC machine fault diagnosis and status is developed. The developed system consists of (1) remote communication module among NC's and host PC using PSTN. (2) 8 channels analog data sensing module, (3) digital I/O module for control or NC machine, (4) communication module between NC machine and remote data communication system via RS-232C, and (5) software man-machine interface. Diagnostic monitoring results generated through a successive type inference engine are displayed in user-friendly graphics. The validity and reliability of the developed system is verified to be a powerful commercial version on a vertical machining center through a series of experiments.