• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.503-510
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• 2002

This paper presents a fault diagnosis method using neural network-based multi-fault models and statistical method to detect and isolate faults in nonlinear systems. In the proposed method, faults are detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. 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. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.220-227
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• 2010

This paper presents a novel fault location scheme on Korean AC electric railway systems. On AC railway system, because of long distance, 40[km] or above, between two railway substations, a fault location technique is very important. Since the fault current flows through the catenary system, it must be modeled exactly to analyze the fault current magnitude and fault location. In this paper, suggesting the novel scheme of fault location, a 9-conductor modeling technique including boost wires and impedance bonds is introduced based on the characteristics of Korean AC electric railway. After obtaining a 9-conductor modeling, the railway system is constructed for computer simulation by using PSCAD/EMTDC. By case studies, we can verify superiority of a new fault location scheme and propose a powerful model for fault analysis on electric railway systems.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.5
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• pp.1-8
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• 2006

The Steer-By-Wire(SBW) system replaces complex mechanical linkages of the current steering system with electric motors, sensors, and electronic control units. However, the SBW system should guarantee its safety and reliability before commercialization, and therefore, a reliable and robust fault-tolerant technology has to be implemented. This paper proposes a fault-tolerant control algorithm for the SBW system. Based on careful analysis on propagation effects of sensor faults, a reliable fault-tolerant control strategy has been developed. The fault-tolerant controller consists of a fault detection part that monitors and detects faults in the steering wheel and road wheel sensors, and a reconfiguration part that switches to normal sensor signal based on fault detection information. It has been demonstrated by simulation that the proposed algorithm detects sensor faults accurately and enables reliable steering control under various dynamic fault situations.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8A
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• pp.569-580
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• 2003

One of the important advantages of multichannel switches is the incorporation of inherent fault tolerance into the switching fabric. For example, if a link which belongs to the multichannel group fails, the remaining links can assume responsibility for some of the traffic on the failed link. On the other hand, if faults occur in the switching elements, it can lead to erroneous routing and sequencing in the multichannel switch. We investigate several fault localization algorithms in multichannel crossbar ATM switches with a view to early fault recovery, The optimal algorithm gives the best performance in terms of time to localization but is computationally complex which makes it difficult to implement. We develop an on-line algorithm which is computationally mote efficient than the optimal algorithm. We evaluate its performance through simulation. The simulation results show that performance of the on line algorithm is only slightly sub-optimal for both random and bursty traffic. Finally a fault recovery algorithm is described which utilizes the information provided by the fault localization algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.35-57
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• 2021

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps. A two way fault-tolerant mechanism i.e., task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

• Journal of the Korea Society for Simulation
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• v.12 no.3
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• pp.69-81
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• 2003

To manage a cluster of distributed server systems, a number of management aspects should be considered in terms of configuration management, fault management, performance management, and user management. System performance monitoring and reporting take an important role for performance and fault management. In this paper, we present distributed system architecture modeling of a performance monitoring and reporting tool. Modeling architecture of four subsystems are introduced: node agent, data collection, performance management & report, and DB schema. The performance-related information collected from distributed servers are categorized into performance counters, event data for system status changes, service quality, and system configuration data. In order to analyze those performance information, we use a number of ways to evaluate data corelation. By using some results from a real site of a company and from simulation of artificial workload, we show the example of performance collection and analysis. Since our report tool detects system fault or node component failure and analyzes performances through resource usage and service quality, we are able to provide information for server load balancing, in short term view, and the cause of system faults and decision for system scale-out and scale-up, in long term view.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.868-870
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• 1996

This paper describes the faun phenomena by the simulation in power system including underground transmission power cable. Studying on fault phenomena is a very important part to decide the circuit breaker, protective relay and system configuration. Simulation was carried out in several different model system depended upon cable kinds using PSCAD/EMTDC, which is one of the transient program. The simulated results show the possibility to analyze transient phenomena for the cable system.

• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.145-150
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• 2015

This paper described the medium voltage high temperature superconducting cable thermal simulation and its application. New simulation method for HTS cable modeling using PSCAD/EMTDC is introduced in this paper. The developed simulation method consists of electrical model part and thermal model part. In electrical model part, power loss and thermal capacitance can be calculated in each layer, then the temperature of each layer can be calculated by power loss and thermal capacitance in thermal model part. This paper also analyzes the electrical and thermal characteristic in the case of normal operating condition and transient including single line to ground fault and line to line ground fault using new simulation method.