• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권3호
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• pp.166-171
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• 2015

In this paper, we measure the current and vibration signals of one-dimensional time series that occur in a motor and pump, respectively. These machines are representative rotary and pumping machines. We also eliminate unnecessary components such as noise by pre-processing the current and vibration signals. Then, in order to diagnose fault signals for the pump and motor, we transform from one-dimensional time series to a two-dimensional phase portrait using Takens’ embedding method. After this transformation, we review the variation in the pattern according to the fault signals.

• 한국지능시스템학회논문지
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• 제16권1호
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• pp.119-123
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• 2006

본 논문에서는, 주성분분석기법과 다층신경망에 기반을 유도전동기의 고장진단기법을 제안하고자 한다. 입력의 수가 많을 경우 다층신경망만을 이용하여 분류하는 데는 한계가 있다. 이러한 문제점을 해결하기 위해 주성분분석기법에 의해 입력특징의 수를 축약한 후, 비선형분류기인 다층신경망을 적용하였다. 또한, 주성 분석기법에 추출된 특징벡터가 고장상태별로 비선형성 특성을 보일 경우 기존의 거리척도 기반에 의한 분류방법으로 정확한 진단을 하는데 어려움이 있다. 이를 위해 비선형 분류기인 MLP를 적용함으로써 효과적인 고장진단을 하자 한다. 세안된 기법은 다양한 실험을 통해 기존의 선형분류기에 비해 우수한 겨과를 보임을 나타내고자 한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권3호
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• pp.178-184
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• 2009

An efficient fault diagnosis system is needed for industry because it can optimize the resources management and improve the performance of the system. In this study, a fault diagnostic system is proposed for rotating machine using wavelet packet transform (WPT) and elman neural network (ENN) techniques. In most fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. In previous work, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the extracted features from the WPT are used as inputs in an Elman neural network. The results show that the scheme can reliably diagnose four different conditions and can be considered as an improvement of previous works in this field.

• 한국지능시스템학회논문지
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• 제21권6호
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• pp.767-774
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• 2011

최근 풍력발전 시스템은 가장 빨리 발전하고 있는 신재생 에너지원중 하나로 각광을 받고 있으며, 세계 선진 국가들뿐만 아니라 국내에서도 개발과 보급에 많은 투자를 하고 있다. 풍력발전 시스템은 블레이드, 발전기 및 인버터 등으로 구성된 복잡한 시스템으로 최근 들어 풍력발전 시스템의 각 구성요소의 고장에 대한 연구가 활발히 진행되고 있다. 풍력발전과 관련된 고장진단은 주로 진동센서로부터의 신호처리에 의해 기계적인 고장을 검출 및 진단하는 것이 주를 이루고 있다. 이에 본 연구에서는 풍력발전시스템에 사용되고 있는 인버터의 고장진단에 적용될 수 있는 기법을 제안하고자 한다. 또한 시뮬레이션 및 실제 시스템에의 적용을 통해 제안된 제안된 기법의 유용성을 확인하고자 한다.

• 한국가스학회지
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• 제8권2호
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• pp.28-34
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• 2004

Enhanced methodologies for process diagnosis and abnormal situation management have been developed for the last two decades. However, there is no single method that always shows better performance over all kinds of diagnostic problems. In this paper, a framework of message-passing, cooperative, intelligent diagnostic agents is presented for improved on-line fault diagnosis through cooperative problem solving of different expertise. A group of diagnostic agents in charge of different process functional perform local diagnoses in parallel; exchange related information with other diagnostic agents; and cooperatively solve the global diagnostic problem of the whole process plant or business units just like human experts would do. For their better understanding, sharing and exchanging of process knowledge and information, we also suggest a way of remodeling processes and protocols, taking into account semantic abstracts of process information and data. The benefits of the suggested multi-agents-based approach are demonstrated by the implementations for solving the diagnostic problems of various chemical processes.

• 한국지능시스템학회논문지
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• 제21권3호
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• pp.317-322
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• 2011

한국정보화진흥원에서 개발한 한국형 성인 인터넷 중독 자가진단 척도인 K-척도는 4가지 요인의 20 문항으로 구성되어 있으며, 사용자의 설문응답값으로 인터넷 중독을 진단한다. 기존의 연구는 대부분 인터넷 중독의 원인을 찾으려는 시도였으며, 청소년 대상으로 수집된 표본을 가지고 그들의 인터넷 중독진단이 수행되었다. 본 연구의 목적은 통계 기법의 주성분분석과 데이터마이닝 기법인 의사결정트리를 이용하여 K-척도의 사용자군 분류를 판정하는 주요인을 발견하는 것이다. 실험 결과로부터 K-척도를 구성하는 4가지 요인 중 내성 및 몰입 요인이 성인 인터넷 중독진단에 가장 큰 영향을 주는 요인임을 알 수 있었다.

• 반도체디스플레이기술학회지
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• 제18권4호
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• pp.87-91
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• 2019

The diagnosis of the failure for the existing electrical facilities was based on regular preventive maintenance, but this preventive maintenance was limited in preventing a lot of cost loss and sudden system failure. To overcome these shortcomings, fault prediction and diagnostic techniques are critical to increasing system reliability by monitoring electrical installations in real time and detecting abnormal conditions in the facility early. As the performance and quality deterioration problem occurs frequently due to the increase in the number of users of the motor pump, the purpose is to build an intelligent control system that can control the motor pump to maximize the performance and to improve the quality and reliability. To this end, a vibration sensor, temperature sensor, pressure sensor, and low water level sensor are used to detect vibrations, temperatures, pressures, and low water levels that can occur in the motor pump, and to build a system that can identify and diagnose information to users in real time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3266-3285
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• 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 춘계학술대회논문집
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• pp.945-948
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• 2005

Mobile computing devices are becoming increasingly prevalent in a huge range of physical area, offering a considerable market opportunity. The focus of this paper is on the development of a platform of fault diagnosis system integrating with personal digital assistant (PDA). An improvement of induction machine rotor fault diagnosis based on AI algorithms approach is presented. This network system consists of two parts; condition monitoring and fault diagnosis by using Artificial Intelligence algorithm. LabVIEW allows easy interaction between acquisition instrumentation and operators. Also it can easily integrate AI algorithm. This paper presents a development environment fur intelligent application for PDA. The introduced configuration is a LabVIEW application in PDA module toolkit which is LabVIEW software.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.486-495
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• 2001

This paper describes an implementation of the interval based expert system for syndrome differential diagnosis of Oriental Traditional Medicine (OTM). An approximate reasoning model using fuzzy logic for syndrome differential diagnosis is proposed. Based on this model, we implemented the system for diagnosing Eight rule diagnosis, organ diagnosis and then final differential syndrome of OTM. After carrying out inference process, the system will provide patient\`s syndromes differentiation diagnosis in the intervals and will give the explanation, which helps the user to understand the obtained conclusions.