• 제어로봇시스템학회논문지
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• 제18권6호
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• pp.540-545
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• 2012

In this paper, we propose a fault diagnosis method using artificial neural network and SVM (Support Vector Machine) to detect and isolate faults in the nonlinear systems. The proposed algorithm consists of two main parts: fault detection through threshold testing using a artificial neural network and fault isolation by SVM fault classifier. In the proposed method a fault is detected when the errors between the actual system output and the artificial neural network nominal system output cross a predetermined threshold. Once a fault in the nonlinear system is detected the SVM fault classifier isolates the fault. The computer simulation results demonstrate the effectiveness of the proposed SVM and artificial neural network based fault diagnosis method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(5)
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• pp.115-118
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• 2001

In this paper we propose an FDI(fault detection and isolation) algorithm using neural network-based multi-fault models to detect and isolate single faults in nonlinear systems. When a change in the system occurs, the errors between the system output and the neural network nominal system output cross a threshold, and once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output.

• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.264-272
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• 1998

This paper proposes a fault detection and recovery mechanism for a fault-tolerant Mini-MAP system, and provides detailed techniques for its implementation. This paper considers the fault-tolerant Mini-MAP system which has dual layer structure from the LLC sublayer down to the physical layer to cope with the faults of those layers. For a good fault detection, a redundant and hierarchical fault supervision architecture is proposed and its implementation technique for a stable detection operation is provided. Information for the fault location is provided from data reported with a fault detection and obtained by an additional network diagnosis. The faults are recovered by the stand-by sparing method applied for a dual network composed of two equivalent networks. A network switch mechanism is proposed to achieve a reliable and stable network function. A fault-tolerant Mini-MAP system is implemented by applying the proposed fault detection and recovery mechanism.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제10권1호
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• pp.83-93
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• 2006

본 논문에서는 유비쿼터스 컴퓨팅 환경에서 규칙 기반의 네트워크 장애 복구 시스템을 제안한다. 이 시스템은 유비쿼터스 컴퓨팅 환경에서 네트워크에 장애가 발생하였을 때, 정해진 규칙을 기반으로 장애를 스스로 복구할 수 있는 장애 관리 시스템이다. 시스템에 적용되는 규칙들을 네트워크 장애의 원인으로 분류하여 제시하였다. 그리고 본 논문에서는 네트워크 장애 복구 시스템이 각 상황에 맞게 규칙이 적용됨을 시뮬레이션을 통해 증명하였다.

• 대한전기학회논문지:전력기술부문A
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• 제52권3호
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• pp.151-157
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• 2003

Wind farm interconnected with grid can supply the power into a power network not only the normal conditions, but also the fault conditions of distribution network. If the fault happened in the distribution power line with wind fm, the fault current level measured in a relaying point might be lower than that of distribution network without wind turbine generator due to the contribution of wind farm. Consequently, it may be difficult to detect the fault happened in the distribution network connected with wind generator This paper describes the effect of the interconnected wind turbine generators on protective relaying of distribution power lines and detection of the fault occurred in a power line network. Simulation results shows that the current level of fault happened in the power line with wind farm depends on the fault impedance, the fault location. the output of wind farm. and the load condition of distribution network.

• 제어로봇시스템학회논문지
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• 제14권4호
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• pp.399-405
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• 2008

Recently, robot technology is actively going on progress to the field of various services such as home care, medical care, entertainment, and etc. Because these service robots are in use nearby person, they need to be operated safely even though hardware and software faults occur. This paper proposes a Fault-Tolerant middleware for a robot system, which has following two characteristics: supporting of heterogeneous network interface and processing of software components and network faults. The Fault-Tolerant middleware consists of a Service Layer(SL), a Network Adaptation Layer(NAL), a Network Interface Layer(NIL), a Operating System ion Layer(OSAL), and a Fault-Tolerant Manager(FTM). Especially, the Fault-Tolerant Manager consists of 4 components: Monitor, Fault Detector, Fault Notifier, and Fault Recover to detect and recover the faults effectively. This paper implements and tests the proposed middleware. Some experiment results show that the proposed Fault-Tolerant middleware is working well.

• 한국정밀공학회지
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• 제14권11호
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

• 한국산업융합학회 논문집
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• 제7권2호
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• pp.199-205
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• 2004

This paper describes the fault diagnosis method to order to construct CIM in complex system with hierarchical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement a special neural network. Fault diagnosis system can forecast faults in a system and decide from the signal information of current machine state. Comparing with other diagnosis system for a single fault, the developed system deals with multiple fault diagnosis, comprising hierarchical neural network (HNN). HNN consists of four level neural network, i.e. first is fault symptom classification and second fault diagnosis for item, third is symptom classification and forth fault diagnosis for component. UNIX IPC is used for implementing HNN with multitasking and message transfer between processes in SUN workstation with X-Windows (Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural network represents a separate process in UNIX operating system, information exchanging and cooperating between each neural network was done by message queue.

• 대한전자공학회논문지
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• 제24권4호
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• pp.610-617
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• 1987

This thesis is concerned with fault-tolerance of a B-net (Beta-network) which is a kind of interconnection network in the multicomputer system. In this paper, a method for obtaining Maximal Tolerable Fault Set(MTFS) of link line connecting switching elements in the arbitrary B-net is presented. Using this method, it is seen that testing of DFA capability is possible when s-a-faults of link line occur, and criterion for determining degree of fault- tolerance of a B-net in terms of link line is introduced.

• 전기학회논문지
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• 제67권9호
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• pp.1146-1151
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• 2018

Recently, researches on the intelligence of electric power facilities have been trying to apply artificial intelligence techniques as computer platforms have improved. In particular, faults occurring in substation should be able to quickly identify possible faults and minimize power fault recovery time. This paper presents fault location technique for 154kV substation using neural network. We constructed a training matrix based on the operating conditions of the circuit breaker and IED to identify the fault location of each component of the target 154kV substation, such as line, bus, and transformer. After performing the training to identify the fault location by the neural network using Weka software, the performance of fault location discrimination of the designed neural network was confirmed.