• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.3-9
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• 2003

The task for chromosome analysis and diagnosis by experienced cytogenetists are being concerned as repetitive, time consuming job and expensive. For that reason, intelligent agent based on chromosome knowledge base has been established to be able to analyze chromosomes and obtain necessary advises from the knowledge base instead of human experts. That is to say, knowledge base by IF THEN production rule was implemented to a knowledge domain with normal and abnormal chromosomes, and then the inference results by knowledge base could enter the inference data into the database. Experimental data were composed of normal chromosomes of 2,736 patients 'cases and abnormal chromosomes of 259 patients' cases that have been obtained from GTG-banding metaphase peripheral blood and amniotic fluid samples. The completed intelligent agent for chromosome knowledge base provides variously morphological information by analysis of normal or abnormal chromosomes and it also has the advantage of being able to consult with user on chromosome analysis and diagnosis.

• 한국지능시스템학회논문지
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• 제17권6호
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• pp.819-825
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• 2007

본 논문에서는 DFT(Discrete Fourier Transform)과 웨이블렛을 이용한 고장진단 알고리즘을 제안한다. 제안된 방법은 주파수 기반의 DFT에 의한 고장패턴의 추출방법과 시간-주파수 기반의 웨이블렛을 이용한 고장패턴의 추출방법을 이용하여 특징점을 추출하였으며, 유도전동기의 최종진단은 DFT와 웨이블렛에 의해 추출된 특징값들을 효과적으로 융합할 수 있는 융합 알고리즘에 의해 수행한다. 개발된 알고리즘은 다양한 실측 데이터에 적응하여 그 타당성을 보였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• 전기학회논문지
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• 제58권7호
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• pp.1276-1280
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• 2009

This paper presents a fault diagnosis expert system for 765kV substation. The proposed system includes the topology processor and intelligent alarm processing subsystems. This expert system estimates the fault section through the inference process using heuristic knowledge and the output of topology processor and intelligent alarm processing system. The rule-base of this expert system is composed of basic rules suggested by Korea Electric Power Corporation and heuristic rules. This expert system is developed using PROLOG language. Also, user friendly Graphic User Interface is developed using visual basic programming in the windows XP environment. The proposed expert system showed a promising performance through the several case studies.

• 전기학회논문지
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• 제59권9호
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• pp.1673-1679
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• 2010

A photovoltaic (PV) generator is significantly regarded as one important alternative of renewable energy systems recently. Fault detection and diagnosis of engineering dynamic systems is a fundamental issue to timely prevent unexpected damages in industry fields. This paper presents an intelligent monitoring approach and fault detection technique for PV generator systems by means of artificial neural network and statistical signal detection theory. We devise a multi-Fourier neural network model for representing dynamics of PV systems and apply a general likelihood ratio test (GLRT) approach for investigating our decision making algorithm in fault detection and diagnosis. We make use of a test-bed of ubiquitous sensor network (USN) based PV monitoring systems for testing our proposed fault detection methodology. Lastly, a real-time experiment is accomplished for demonstrating its reliability and practicability.

• 한국정보통신학회논문지
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• 제7권3호
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• pp.526-531
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• 2003

교량 진단을 위한 새로운 Linux 실장 지능형 제어기 및 원격 모니터링 시스템을 구현한다. Linux 실장 지능형 제어기의 하드 코어는 32비트 CPU로서 구성되었고 교량 진단을 위해 실시간 모니터링과 FFT를 수행 할 수 있도록 설계되었다. 그리고 모니터링 시스템은 Java에 의한 인터넷 환경 및 GUI 환경에서 수행되도록 설계되었다. 상세 설계와 기능적 해석을 시스템 기반에서 수행되었다.

• 한국생산제조학회지
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• 제7권4호
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• pp.42-49
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• 1998

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surface from which the particles originated. The morphological identification of wear debris can therefore provide very early detection of a fault and can also often facilitate a diagnosis. The purpose of this study is to attempt the developement of intelligent system for moving condition diagnosis of the machine driving system. The four shape parameter(50% volumetric diameter, aspect, roundness and reflectivity) of war debris are used as inputs to the neural network and learned the moving condition of five values(material3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the moving condition and materials very well by neural network.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 추계학술발표회논문집(1)
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• pp.443-448
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• 1997

The intelligent software system for nuclear power plants (NPPs) has been conceptually designed in this study. Its design goals are to operate NPPs in n improved manner and to support operators' cognitive tasks. It consists of six major modules such as "Information Processing," "Alarm Processing," "Procedure Tracking," "Performance Diagnosis," and "Event Diagnosis" modules for operators and "Malfunction Diagnosis" module for maintenance personnel. Most of the modules have been developed for several years and the others are under development. After the completion of development, they will be combined into one system that would be main parts of advanced control rooms in NPPs. that would be main parts of advanced control rooms in NPPs.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.597-608
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• 1998

A diagnosis system that provides early warnings regarding machine malfunction is very important for rolling mill so as to avoid great losses resulting from unexpected shutdown of the production line. But it is very difficult to provide early warnings in rolling mill. Because dynamics of rolling mill is non-linear. This paper proposes a new method for diagnosis of rolling mill using wavelet to solve this problem. Proposed method that measures the vibration signals of rolling mill on-line and analyze it using wavelet to acquire pattern datas. And we design a nero-fuzzy model that diagnose a rolling mill using this data. Validity of the new method is asserted by numerical simulation.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.463-467
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• 2002

본 논문에서는 사출 성형기 Barrel 부분에 인공지능 알고리즘을 적용하여 고장 검출 및 진단 시스템을 구성하였다. 고장 검출 및 진단을 위한 실시간 계측 시스템을 구축하였고, 계측된 데이터를 SQL-2000 Server를 사용하여 사출 성형기 Barrel의 이력 데이터베이스를 구축하였다 기존의 시스템이 단 시간의 시스템 정보를 습득하여 고장을 검출하고 진단한 것에 비해 본 연구에서는 장시간의 데이터를 습득하여 고장 검출 및 진단에 신뢰성을 높일 수있었다 고장 진단에 필요한 데이터는 실제 시스템의 운전에서 실시간으로 습득하였고, 데이터의 신뢰성을 높이기 위해 사출 성형기의 데이터와 정밀 계측기의 데이터를 Database에 저장하였다. 고장 검출 및 진단을 위하여 Fuzzy 알고리즘을 사용하여 신뢰성 있는 진단을 수행하였다.