• 전자공학회논문지B
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• 제32B권3호
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• pp.1-10
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• 1995

In this paper, a fault-tolerant Mini-MAP system with high reliability is proposed. For fault-tolerance, the LLC sublayer, MAC sublayer, and physical layer of the Mini-MAP system are dualized. The detection of faults, the replacement of the failed network, and the management of the network are three major functions required for the dualization, and they are performed by ESM(Error Supervisory Machine), EMM(Error Management Machine), and NMM(Network Management Machine) of the proposed fault-tolerant Mini-MAP system, respectively. The ring maintenance function of the MAC sublayer is used for the detection of the faults. In the proposed fault-tolerant Mini-MAP system, the data are received from both of the dualized networks and transmitted to the selected one of the two. We analyze the reliability and the MTTF(Mean Time To Failure) of the proposed fault-tolerant Mini-MAP system and show that it has better performance compared to a general Mini-MAP system.

• 한국안전학회지
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• 제15권2호
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• pp.36-45
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• 2000

Automated production system is composed of many complicated techniques and it become a very difficult task to control, monitor and diagnose this compound system. Moreover, it is required to develop an effective diagnosing technique and reduce the diagnosing time while operating the system in parallel under many faults occurring concurrently. This study develops a Modular Artificial Neural Network(MANN) which can perform a diagnosing function of multiple faults with the following steps: 1) Modularizing a complicated system into subsystems. 2) Formulating a hierarchical structure by dividing the subsystem into many detailed elements. 3) Planting an artificial neural network into hierarchical module. The system developed is implemented on workstation platform with $X-Windows^{(r)}$ which provides multi-process, multi-tasking and IPC facilities for visualization of transaction, by applying the software written in $ANSI-C^{(r)}$ together with $MOTIF^{(r)}$ on the fault diagnosis of PI feedback controller reactor. It can be used as a simple stepping stone towards a perfect multiple diagnosing system covering with various industrial applications, and further provides an economical approach to prevent a disastrous failure of huge complicated systems.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권3호
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• pp.173-180
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• 2016

Fault detection and diagnosis is a task to monitor the occurrence of faults and pinpoint the exact location of faults in the system. Fault detection and diagnosis is gaining importance in development of efficient, advanced and safe industrial systems. Three phase inverter is one of the most common and excessively used power electronic system in industries. A fault diagnosis system is essential for safe and efficient usage of these inverters. This paper presents a fault detection technique and fault classification algorithm. A new feature extraction approach is proposed by using three-phase load current in three-dimensional space and neural network is used to diagnose the fault. Neural network is responsible of pinpointing the fault location. Proposed method and experiment results are presented in detail.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.405-407
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• 1994

In order to provide reliable power service and to prevent a potentail hazard and damage, it is important to detect high impedance fault in power distribution line. This paper presents a neural network based approach for the detection of high impedance faults. A time delay neural network has been selected and trained for the fault currents obtained from field experiments. Detection experiments have been performed with the data from four different high impedance surfaces. Experimental results indicated the feasibility of using TDNN for the detection of high impedance faults.

• 한국정밀공학회지
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• 제33권12호
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• pp.985-992
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• 2016

Bearings have various uses in industrial equipment. The lifetime of bearings is often lesser than anticipated at the time of purchase, due to environmental wear, processing, and machining errors. Bearing conditions are important, since defects and damage can lead to significant issues in production processes. In this study, we developed a method to diagnose faults in the bearing conditions. The faults were determined using kurtosis, average, and standard deviation. An intrinsic mode function for the data from the selected axis was extracted using empirical mode decomposition. The intrinsic mode function was obtained based on the frequency, and the learning data of ANN (Artificial Neural Network) was concluded, following which the normal and fault conditions of the bearing were classified.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.351-363
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• 2008

This paper is concerned with tasking and guidance of networked airborne sensors to achieve fault-tolerant sensing. The sensors are coordinated to locate hostile transmitters by intercepting and processing their signals. Faults occur when some sensor-carrying vehicles engaged in target location missions are lost. Faults effectively change the network architecture and therefore degrade the network performance. The first objective of the paper is to optimally allocate a finite number of sensors to targets to maximize the network life and availability. To that end allocation policies are solved from relevant Markov decision problems. The sensors allocated to a target must continue to adjust their trajectories until the estimate of the target location reaches a prescribed accuracy. The second objective of the paper is to establish a criterion for vehicle guidance for which fault-tolerant sensing is achieved by incorporating the knowledge of vehicle loss probability, and by allowing network reconfiguration in the event of loss of vehicles. Superior sensing performance in terms of location accuracy is demonstrated under the established criterion.

• 한국소음진동공학회논문집
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• 제12권10호
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• pp.799-807
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• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• 정보처리학회논문지C
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• 제9C권4호
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• pp.497-504
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• 2002

본 논문에서는 시스템의 네트워크 구성 장애를 관리하기 위한 관리 모델과 에이전트들 간의 협력을 통한 장애의 진단 및 복구 알고리즘을 제시하고 있다. 관리 모델에는 장애의 검출, 진단, 복구의 세 단계로 이루어지며 각각은 RBR(Rule-Based Reasoning)에 기반으로 하여 규칙기반 지식 데이터베이스에 있는 규칙을 이용하여 네트워크의 구성 장애를 진단하고 복구한다. 또한 관리 도메인 상의 네트워크에 분포하고 있는 여러 에이전트들 간의 협력을 통하여 시스템 단독으로는 해결할 수 없는 복잡한 문제를 해결하거나 네트워크의 상황까지 고려하여 진단하고 복구함으로써 효율적인 시스템의 네트워크 구성 관리 알고리즘을 제시하고 있다.

• 자원환경지질
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• 제33권1호
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• pp.61-75
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• 2000

Through modeling fault network using thin plate finite element technique in the San Andreas Fault system with slip rate over 1mm/year, as well as elevation, heat flow, earthquakes, geodetic data and crustal thickness, we compare the results with velocity boundary conditions of plate based on the NUVEL-1 plate model and the approximation of deformation in the Great Basin region. The frictional and dislocation creep constants of the crust are calculated to reproduce the observed variations in the maximum depth of seismicity which corresponds to the temperature ranging from $350^{\circ}C$ to $410^{\circ}C$. The rheologic constants are defined by the coefficient of friction on faults, and the apparent activation energy for creep in the lower crust. Two parameters above represent systematic variations in three experiments. The pattern of model indicates that the friction coefficient of major faults is 0.17~0.25. we test whether the weakness of faults is uniform or proportional to net slip. The geologic data show a good agreement when fault weakness is a trend of an additional 30% slip dependent weakening of the San Andreas. The results of study suggest that all weakening is slip dependent. The best models can be explained by the available data with RMS mismatch of as little as 3mm/year, so their predictions can be closely related with seismic hazard estimation, at least along faults where no data are available.