• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1727-1731
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• 2003

In this paper we proposed backward fuzzy Petri-net to diagnoses faults in power systems by using the time sequence information of protection system. As the complexity of power systems increases, especially in the case of multiple faults or incorrect operation of protective devices, fault diagnosis requires new and systematic methods to the reasoning process, which improves both its accuracy and its efficiency. The fuzzy Petri-net models of protection system are composed of the operating process of protective devices and the fault diagnosis process. Fault diagnosis model, which makes use of the nature of fuzzy Petri-net, is developed to overcome the drawbacks of methods that depend on operator knowledge. The proposed method can reduce processing time and increase accuracy when compared with the traditional methods. And also this method covers online processing of real-time data from SCADA (Supervisory Control and Data Acquisition)

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.960-968
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• 2005

The high cost in maintaining complex manufacturing process makes it necessary to enhance an efficient maintenance system. For the effective maintenance of manufacturing process, precise fault diagnosis should be performed and an appropriate maintenance action should be executed. This paper suggests an intelligent fault diagnosis system using hybrid data mining. In this system, the rules for the fault diagnosis are generated by hybrid decision tree/genetic algorithm and the most effective maintenance action is selected by decision network and AHP. To verify the proposed intelligent fault diagnosis system, we compared the accuracy of the hybrid decision tree/genetic algorithm with one of the general decision tree learning algorithm(C4.5) by data collected from a coil-spring manufacturing process.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.963-968
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• 2003

The continuous process systems usually consists of various components: driven rollers. idle rolls, load-cell and so on. Even a simple fault in a single component in the line may cause a catastrophic damage on the final products. Therefore it is absolutely necessary to diagnosis the components of the continuous systems. In this paper, an adaptive eccentricity compensation method is presented. And a new diagnosis method for transverse roll shape defects on rolling process is developed. The new method was induced from analyzing the rolling mechanism by using rolling force model, tension model, Hitchcock's equation, and measured delivery thickness of materials etc. Computer simulation results also show that the proposed diagnosis methods is very effective in the diagnosis of 3-D roll shape

• Journal of the Korean Institute of Gas
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• v.4 no.4 s.12
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• pp.42-49
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• 2000

Due to their size and complexity, it is very difficult to make diagnostic system for the whole chemical processes. Therefore, a systematic approach is required to decompose larpge-scale process into sub-processes and then diagnose them. This paper suggests a method for the minimization of knowledge base and flexible diagnosis to be used in qualitative fault diagnosis based on Fault-Effect Tree model. The system can be decomposed for flexible diagnosis, size reduction of knowledge base, and consistent construction of complex knowledge base. The new node, gate-variable, is introduced to connect the cause-effect relationships of each sub-process. For on-line diagnosis, off-line analysis is performed to construct Fault-Effect Trees of gate-variables as well as activation conditions of gate-variables. On-line diagnosis strategy is modified to get the same diagnosis result without system decomposition. The proposed method is illustrated with a fault diagnosis system for a large-scale boiler plant.

• Journal of IKEEE
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• v.13 no.1
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• pp.65-70
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• 2009

We propose an ultrashort pulse optical code-division multiple-access (O-CDMA) scheme based on a pseudorandom binary M-sequence spectral phase encoding and decoding of coherent mode-locked laser pulses and perform a numerical simulation to analyze its feasibility. We demonstrate the ability to properly decode any of the multiple (eight) 10 Gbit/s users by the matched code selection of the spectral phase decoder. The peak power signal to noise ratio of properly and improperly decoded $8{\times}10 Gb/s$ signals could be greater than 15 for 127 M-sequence coding.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.5
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• pp.450-457
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• 2004

In this research, the automatic synthesis of knowledge models is proposed. which are the basis of the methods using qualitative models adapted widely in fault diagnosis and hazard evaluation of chemical processes. To provide an easy and fast way to construct accurate causal model of the target process, the Role-Behavior modeling method is developed to represent the knowledge of modularized process units. In this modeling method, Fault-Behavior model and Structure-Role model present the relationship of the internal behaviors and faults in the process units and the relationship between process units respectively. Through the multiple modeling techniques, the knowledge is separated into what is independent of process and dependent on process to provide the extensibility and portability in model building, and possibility in the automatic synthesis. By taking advantage of the Role-Behavior Model, an algorithm is proposed to synthesize the plant-wide causal model, Fault-Causality Graph (FCG) from specific Fault-Behavior models of the each unit process, which are derived from generic Fault-Behavior models and Structure-Role model. To validate the proposed modeling method and algorithm, a system for building FCG model is developed on G2, an expert system development tool. Case study such as CSTR with recycle using the developed system showed that the proposed method and algorithm were remarkably effective in synthesizing the causal knowledge models for diagnosis of chemical processes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.457-462
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• 1995

This paper describes diagnosis strategy of the Flexible Multiple Fault Diagnosis Module for forecasting faults in system and deciding current machine state form sensor information. Most studydeal with diagnosis control stategy about single fault in a system, this studies deal with multiple fault diagnosis. This strategy is consist of diagnosis control module such as backward tracking expert system shell, various neural network, numerical model to predict machine state and communication module for information exchange and cooperate between each model. This models are used to describe structure, function and behavior of subsystem, complex component and total system. Hierarchical structure is very efficient to represent structural, functional and behavioral knowledge. FT(Fault Tree). ST(Symptom Tree), FCD(Fault Consequence Diagrapy), SGM(State Graph Model) and FFM(Functional Flow Model) are used to represent hierachical structure. In this study, IA(Intelligent Agent) concept is introduced to match FT component and event symbol in diagnosed system and to transfer message between each event process. Proposed diagnosis control module is made of IPC(Inter Process Communication) method under UNIX operating system.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.340-345
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• 1996

Because of limited resources of time and information processing capability during abnormal situation, diagnosis is difficult tasks in nuclear power plant (NPP) operators. Moreover since minimizing of adverse consequences according to process abnormalities is vital for the safety of NPP, introducing of diagnosis support systems have particularly emphasized. However, considerable works to develop effective diagnostic support system are not sufficiently fulfilled because of the complexity of NPP is one of the major problems. To cope with this complexity, a lot of model-based diagnosis support systems have considered and implemented worldwide. In this paper, as a prior step to development of model-based diagnosis support systems, primary side of pressurized water reactor is functionally modeled by multilevel flow modeling (MFM) concept. MFM is suitable for complex system modeling and for diagnosis of abnormalities. Furthermore, knowledge-based diagnosis process, of NPP operator could be supported because this diagnosis strategy can represent operator's one.

• Journal of Korean Society for Quality Management
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• v.20 no.1
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• pp.91-100
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• 1992

A diagnosis system for naphtha reforming process has been designed and implemented using expert system building technique. The system is composed of knowledge base, inference engine, user interface, database and database interface. The concept and the method of this system may be applied to development of other systems for the reforming process.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.147-153
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• 1997

Fault diagnosis technique in machining system which is one of engineering techniques absolutely necessary to automation of manufacturing system has been proposed. As a whole, diagnosis process is explained by two steps: sensor data acquisition and reasoning current state of system with the given sensor data. Flexible disk grinding process implemented in milling machine was employed in order to obtain empirical manufacturing process information. Resistance force data during machining were acquired using tool dynamometer known as sensor which is comparably accurate and reliable in operation. Tool status during the process was analyzed using influnece diagram assigning probability from the statistical analysis procedure.