• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 전기설비전문위원
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• pp.216-218
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• 2009

In this paper, a real time monitoring and management system being operated in the ubiquitous environment was developed to monitor leakage current, load current, and arc-fault, and an electrical safety network for reasonable management of electrical risk factor was proposed. For confirmation of usefulness and reliability of the proposed safety network and system, the developed intelligent panels were applied to 28 Korean traditional houses in Jeonjoo city, and the network including the panels was operated. If the National Electrical Safety Network is completely constructed in the houses of general electrical users, the network will have an effect on that a main manager transfers from general people to expert. As a result, the electrical fires caused by an over-load, an arc-fault, and an earth-fault will be prevented.

• 대한안전경영과학회지
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• 제4권4호
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• pp.97-108
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• 2002

This study deals with the application of knowledge-based engineering and a methodology for the assessment & measurement of reliability, availability, maintainability, and safety of industrial systems using fault-tree representation. A fuzzy methodology for fault-tree evaluation seems to be an alternative solution to overcome the drawbacks of the conventional approach. To improve the quality of results, the membership functions must be approximated based on heuristic considerations. Conventionally, it is not always easy to obtain a system reliability for components with different individual failure probability density functions(p.d.f.), We utilize fuzzy set theory to solve the adequacy of the conventional probability in accounting and processing of built-in uncertainties in the probabilistic data. The purpose of this study is to propose the framework of knowledge-based engineering through integrating the various sources of knowledge involved in a FTA.

• 대한안전경영과학회지
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• 제12권2호
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• pp.65-73
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• 2010

In an automated industry PLC plays a central role to control the manufacturing system. Therefore, fault free operation of PLC controlled manufacturing system is essential in order to maximize a firm's productivity. On the contrary, distributed nature of manufacturing system and growing complexity of the PLC programs presented a challenging task of designing a rapid fault finding system for an uninterrupted process operation. Hence, designing an intelligent monitoring, and diagnosis system is needed for smooth functioning of the operation process. In this paper, we propose a method to continuously acquire a stream of PLC signal data from the normal operational PLC-based manufacturing system and to generate diagnosis model from the observed PLC signal data. Consequently, the generated diagnosis model is used for distinguish the possible abnormalities of manufacturing system. To verify the proposed method, we provided a suitable case study of an assembly line.

• 전기학회논문지
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• 제67권1호
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• pp.52-60
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• 2018

The winding of the motor stator coil is broken due to external stress and various factors. If the proper current is not injected when interturn fault(ITF) occurs, the fault can easily be expanded and the motor can be finally destroyed, resulting in many problems with time costs and safety. In this paper, the power loss limit concept, which is the inherent durability of each motor, is applied to secure safety by controlling the total power loss of the motor within the limits. So, we propose an algorithm that can control maximum torque per minimum power loss based on constant torque curve and power loss limit. To verify the proposed method, the simulation and experimental results with an Interior permanent magnet synchronous motor(IPMSM) having an ITF are shown.

• 한국안전학회지
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• 제21권1호
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• pp.6-14
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• 2006

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

• Nuclear Engineering and Technology
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• 제36권3호
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• pp.210-218
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• 2004

In the interest of nuclear power plant safety, a self-diagnostic monitoring system (SDMS) is needed to monitor defects in safety-related components. An air-operated valve (AOV) is one of the components to be monitored since the failure of its operation could potentially have catastrophic consequences. In this paper, a model of the AOV is developed with the parameters that affect the operational characteristics. The model is useful for both understanding the operation and correlating parameters and defects. Various defects are introduced in the experiments to construct a fault library, which will be used in a pattern recognition approach. Finally, the validity of the fault library is examined.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.126-134
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• 2008

As many systems depend on electronics, concern for fault tolerance is growing rapidly. For example, a car with its steering controlled by electronics and no mechanical linkage from steering wheel to front tires (steer-by-wire) should be fault tolerant because a failure can come without any warning and its effect is devastating. In order to make system fault tolerant, there has been a body of research mainly from aerospace field. This paper presents the structure of predictive hybrid redundancy that can remove most erroneous values. In addition, several numerical simulation results are given where the predictive hybrid redundancy outperforms wellknown average and median voters.

• 대한안전경영과학회지
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• 제3권2호
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• pp.113-121
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• 2001

Today, facilities are composed of many complex components or parts. Because of this characteristics, the frequency of failures is decreasing, but the strength of failures is increasing; therefore, the failure analysis about many complex components or parts was needed. In the former research about Fault Tree Analysis, failure data of similar facilities have been used for forecasting about target system or components, but in case that the system or components for forecasting failure is new or qualitative and quantitative data are given simultaneously, there are many difficulty in using Fault Tree Analysis with this incorrect failure data. Therefore, this paper deal with the Fault Tree Analysis method which be applied with Fuzzy theory in above case. In case that , therefore, if there is no the correct failure data, it is represented a system or components as qualitative variable. subsequently, it converted to the quantitative value using fuzzy theory, and the values used as the value for failure forecast.

• 한국안전학회지
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• 제20권3호
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• pp.64-70
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• 2005

In this literature, fault detection and classification of faulty induction motors are carried out through Z-index and frequency analysis. Above frequency analysis refer Fourier transformation and Wavelet transformation. Z-index is defined as the similar form of energy function, also the faulty and healthy conditions are classified through Z-index. For the detection and classification feature extraction for the fault detection of an induction motor is carried out using the information from stator current. Fourier and Wavelet transforms are applied to detect the characteristics under the healthy and various faulty conditions. We can obtain feature vectors from two transformations, and the results illustrate that the feature vectors are complementary each other.

• Nuclear Engineering and Technology
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• 제53권1호
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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.