• 한국통신학회논문지
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• 제32권4A호
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• pp.379-386
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• 2007

본 논문에서는 효율적인 다중 고착 고장 진단 알고리듬을 제안하겠다. 제안하는 고장 진단 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. 이 알고리듬은 ISCAS85회로와 완전 주사 스캔이 삽입된 ISCAS89회로에서 실험하여 성능을 입증하였다.

• 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).

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.87-87
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• 2000

In this paper, an integrated design of fault detection, diagnosis and reconfigurable control tot multi-input and multi-output system is proposed. It is based on the interacting multiple model estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural and/or parametric changes. This research focuses on the method to recover the performance of a system with failed actuators by switching plant models and controllers appropriately. The proposed scheme is applied to a fault tolerant control design for flight control system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.125-127
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• 2000

Multiple faults are hard to diagnose correctly because the operation of circuit breakers tripped by former fault changes the topology of power systems. The information including operating time of actuated relays and tripped circuit breakers is used for considering changes of the network topology in fault section diagnosis. This paper presents a method for fault section diagnosis using a set of matrices which represent changes of the network topology due to operation of circuit breakers. The proposed method uses fuzzy relation to cope with the unavoidable uncertainties imposed on fault section diagnosis of power systems. The inference executed by the proposed matrices provides the fault section candidates in the form of a matrix made up of the degree of membership. Experimental studies for real power systems reveal usefulness of the proposed technique to diagnose multiple faults.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.287-290
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• 1996

Fault diagnosis problem is currently the subject of extensive research and numerous survey paper can be found. Although several works are studied on the fault detection and isolation observers and the residual generators, those are concerned with only the detection of actuator failures or sensor failures. So, the perfect detection and isolation is strongly required for practical applications. In this paper, a, strategy of fault diagnosis using multiple proportional integral (PI) observers including the magnitude of actuator failures is provided. It is shown that actuator failures are detected and isolated perfectly by monitoring the integrated error between actual output and estimated output by a PI observer. Also in presence of complex actuator and sensor failures, these failures are detected and isolated by multiple PI observers.

• 제어로봇시스템학회논문지
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• 제9권3호
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• pp.251-258
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• 2003

Optical Switching Matrix (OSM) or Optical Multistage Interconnection Networks (OMINs) comprising photonic switches have been studied extensively as important interconnecting blocks for Optical Cross Connects (OXC) based on Optical Burst Switching (OBS). A basic element of photonic switching networks is a 2$\times$2 directional coupler with two inputs and two outputs. This paper is concerned with the diagnosis of multiple crosstalk-faults in OSM. As the network size becomes larger in these days, the conventional diagnosis methods based on tests and simulation become inefficient, or even more impractical. We propose a simple and easily implementable algorithm for detection and isolation of the multiple crosstalk-faults in OSM. Specifically. we develop an algorithm for isolation of the source fault in switching elements whenever the multiple crosstalk-faults arc detected in OSM. The proposed algorithm is illustrated by an example of 16$\times$16 OSM.

• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.790-795
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• 1994

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item, component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introducd to Hierarchical Artificial Neural Network(HANN) for this purpose. HANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification,forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trainined by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing HANN with multitasking and message transfer between processes in SUN workstation. We tested HANN in reactor system.

• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1614-1622
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• 2018

The research related to fault diagnosis in permanent magnet synchronous motors (PMSMs) has attracted considerable attention in recent years because various faults such as permanent magnet demagnetization and short-circuited turns can occur and result in unexpected failure of motor related system. Several conventional current and back electromotive force (BEMF) analysis techniques were proposed to detect certain faults in PMSMs; however, they generally deal with a single fault only. On the contrary, cases of multiple faults are common in PMSMs. We propose a fault diagnosis method for PMSMs with single and multiple combined faults. Our method uses three phase BEMF voltages based on the fast Fourier transform (FFT), support vector machine(SVM), and visualization tools for identifying fault types and severities in PMSMs. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to visualize the high-dimensional data into two-dimensional space. Experimental results show good visualization performance and high classification accuracy to identify fault types and severities for single and multiple faults in PMSMs.

• 한국정밀공학회지
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• 제14권2호
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• pp.41-47
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• 1997

Fault diagnosis problem is currently a subject of extensive research in the control field. Although there are several works on the fault detection and isolation observers and the residual generators, those are con- cerned with only the detection of actuator failures or sensor failures. So, the perfect detection and isolation for the actuator and sensor failures is strongly required in the field of the practical applications. In this paper, a strategy of fault diagnosis using multiple proportional integral (PI) observers including the magnitude of actuator failures is provided. It is shown that actuator failures are detected and isolated perfectly by monitoring the integrated error between actual output and estimated output by a PI observer. Also in presence of complex actuator and sensor failures, these failures are detected and isolated by multiple PI observers.