• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2004년도 춘계학술대회
• /
• pp.822-827
• /
• 2004

In this paper, an intelligent fault diagnosis system is proposed for induction motors through the combination of feature extraction, genetic algorithm (GA) and neural network (ANN) techniques. Features are extracted from motor vibration signals, while reducing data transfers and making on-line application available. GA is used to select most significant features from whole feature database and optimize the ANN structure parameter. Optimized ANN diagnoses the condition of induction motors online after trained by the selected features. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origin on the induction motors. The results of the test indicate that the proposed system is promising for real time application.

• 한국지능시스템학회논문지
• /
• 제19권5호
• /
• pp.706-712
• /
• 2009

본 논문에서는 프레스공정라인에서 발생하는 고장을 감지하고 분류하기 위한 고장진단기법을 제안한다. 또한 윤활유 레벨을 자동감지 하기 위한 방법도 제안하다. 제안한 방법에서는 FFT 주파수해석과 여러 경계인수를 갖는 ART2 신경회로망을 사용하며, LabVIEW를 이용하여 고장진단 및 윤활유 레벨 자동감시를 위한 GUI(Graphical User Interface) 프로그램을 제작하여 고장진단을 수행하였다. 실험결과들로부터 제안한 유도전동기 고장진단 및 윤활유 레벨 자동감시시스템의 성능을 확인하였다.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2002년도 춘계학술대회논문집
• /
• pp.39-52
• /
• 2002

Research Direction The significant research direction in mechanical fault diagnosis area: Theorles and approaches for fault feature extracting and fault classification. Identification Complicated fault generating mechanism and its model Intelligent fault diagnosis system (including the expert system and network based remote diagnosis system) One of the Key Points： Fault feature extracting techniques based on (modern) signal processing(omitted)

• 한국정보통신학회논문지
• /
• 제7권3호
• /
• pp.526-531
• /
• 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
• /
• 제7권4호
• /
• pp.42-49
• /
• 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
• /
• 한국원자력학회 1997년도 추계학술발표회논문집(1)
• /
• pp.443-448
• /
• 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.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1995년도 춘계학술대회 논문집
• /
• pp.457-462
• /
• 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.

• 한국전자통신학회논문지
• /
• 제11권1호
• /
• pp.59-64
• /
• 2016

본 논문에서는 주택의 전기화재 방지를 위해 아크고장전류로부터 아크 진단 기능을 가지는 지능형 분전반이 개발된다. 지능형 분전반의 주 제어장치는 단상전력관리를 지원하는 단상 전력관리 장치와 아크전류로부터 아크 고장을 진단하기 위한 아크 진단 장치로 구성된다. 본 논문에서는 단상 전력관리 장치와 아크진단장치의 시작품이 설계, 제작되며, 전기화재의 원인을 분전반으로부터 원격 서버 시스템에 전송하기 위해서 주제어장치와 아크 고장 진단 장치와의 연동기능이 개발된다.

• 한국안전학회지
• /
• 제19권1호
• /
• pp.23-30
• /
• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• International Journal of Internet, Broadcasting and Communication
• /
• 제13권3호
• /
• pp.92-103
• /
• 2021

With the continuous acceleration of economic and social development, people gradually pay attention to their health, improve their living environment, diet, strengthen exercise, and even conduct regular health examination, to ensure that they always understand the health status. Even so, people still face many health problems, and the number of chronic diseases is increasing. Recently, COVID-19 has also reminded people that public health problems are also facing severe challenges. With the development of artificial intelligence equipment and technology, medical diagnosis expert systems based on big data have become a topic of concern to many researchers. At present, there are many algorithms that can help computers initially diagnose diseases for patients, but they want to improve the accuracy of diagnosis. And taking into account the pathology that varies from person to person, the health diagnosis expert system urgently needs a new algorithm to improve accuracy. Through the understanding of classic algorithms, this paper has optimized it, and finally proved through experiments that the combined classification algorithm improved by latent factors can meet the needs of medical intelligent diagnosis.