• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.218-226
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• 1998

In this study a neural network based expert system designed to diagnose operating status of a rotating spindle system supported by ball bearings was introduced. In order to facilitate practical failure situations, five exemplary abnormal status was fabricated. Out of several possible data source locations, seven most effective spots were chosen and proven to be the most successful in predicting single and multiple abnormalities. Increased signal strength was measured around where abnormality was embedded. Signal mea-surement locations producing high prediction rate were also classified. Even though multiple abnormalities were hard to be decoupled into their individual causes, proposed diagnostic system was somewhat effective in predicting such cases under certain combination of sensor locations. Among several abnormal operating conditions, highest prediction rate can be expected when signal is spoiled by the failure or damage in outer race. Proposed diagnostic system was again proven to be the most effective system in analyzing and ranking the importance of data sources.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.5
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• pp.205-211
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• 2005

Recently, power system is getting larger and more complex. When the complex power system has a problem, it is very difficult even for the experts to find out where the problem is and to make a timely decision by operators. There have been many studies on these problems but the results are not good enough for applying to real power system. Therefore, power system operators always had to judge the exact state of power system and be preparative for the problems that can occur later. We developed new methods that can be applied to complex power system by dividing the system into small modules. By using 'module', we can combine small modules together to make complex power systems and the knowledge base that is applied to fault diagnosis system. As a result, compared to previously developed diagnosis products, operation time is shortened and the knowledge base is become simpler and clearer, which made online usage capable. This system can be used as a complementary measurement that helps the operator from making any mistakes.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.28 no.2
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• pp.144-150
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• 1992

This paper describes an application of artificial neural network to diagnose the defects of rotating machiner. Induction motor was used to the object of defect diagnosis. For defect diagnosis, the frequency spectrum of vibration was utilized. Learning method of applied neural network was back propagation. Neural network has following advantage; Once it has been learned, inference time is very short and it can provide a reasonable conclusion regardless of insufficient input data. So, this defect diagnosis system can be used superiorly to rule based expert system as quality inspection of rotating machinery in the shop.

• Journal of Astronomy and Space Sciences
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• v.14 no.1
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• pp.166-178
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• 1997

Many research works are on going to monitor and diagnose diverse malfunctions of satellite systems as the complexity and number of satellites increase. Currently, many works on monitoring and diagnosis are carried out by human experts but there are needs to automate much of the routine works of them. Hence, it is necessary to study on using expert systems which can assist human experts routine work by doing automatically, thereby allow human experts devote their expertise more critical and important areas of monitoring and diagnosis. In this paper, we are employing artificial intelligence techniques to model human expert's knowledge and inference the constructed knowledge. Especially, case-based approaches are used to construct a knowledge base to model human expert capabilities which use previous typical exemplars. We have designed and implemented a prototype case-based system for diagnosing satellite malfunctions using cases. Our system remembers typical failure cases and diagnoses a current malfunction by indexing the case base. Diverse methods are used to build a more user friendly interface which allows human experts can build a knowledge base in an easy way.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.11-19
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• 2014

In this paper, we design an intelligent agent system with the data mining module and information fusion module as the core components of the system and investigate the possibility for the medical expert system. In the data mining module, fuzzy neural network, OFUN-NET analyzes multi-view data and produces fuzzy cluster knowledge base. In the information fusion module and application module, they serve the diagnosis result with possibility degree and useful information for diagnosis, such as uncertainty decision status or detection of asymmetry. We also present the experiment results on the BI-RADS-based feature data set selected form DDSM benchmark database. They show higher classification accuracy than conventional methods and the feasibility of the system as a computer aided diagnosis system.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.65-65
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• 2002

본 논문에서는 용해로 이상감시를 위한 실시간 유리 용해로 운전 전문가시스템을 구축한 결과를 소개한다. 유리 용해 공정에서는 운전자의 경험지식에 의해 내부의 상황을 판단하게 되고, 이는 용해로 수명과 제품의 품질에 중요한 영향을 준다. 이를 전문가 시스템으로 구현하기 위하여, 먼저 기존 운전자의 지식을 취합, 분석한다. 그 후,취합된 각 지식들의 특성에 부합하도록 이진 규칙(Crisp Rule)과 퍼지 규칙(Fuzzy Rule)으로 구분한다. 이 때, 선형 회귀분석을 통하여 퍼지 규칙의 입력을 결정함으로써 보다 정확한 운전 지식의 표현이 가능하도록 하였다. 설계된 알고리듬은 젠심(Gensym)사의 실시간 전문가 시스템 개발 툴인 G2를 사용하여 구현하였다. 제시된 퍼지 전문가 시스템은 삼성코닝(주) 수원사업장의 실제 생산 용해 공정에 직접 적용하여 그 효율성이 검증되었다.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.880-887
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• 2005

This paper presents a fault diagnosis(detection and isolation) approach for the Ammunition Transport Automation system(ATAS). Due to limited time and information available during its cyclic operation, the on-line fault detection algorithm consists of sequential test logics referring to the normal states, which can be considered as a kind of expert system. If a failure were detected, the off-line isolation algorithm finds the fault location through trained ART2 neural network. By the results of simulations and some on-line field test, it has been shown that the presented approach is effective enough and applicable to related automation systems.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.1109-1114
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• 1992

Modern chemical processes are becoming more complicated. The sophisticated chemical processes have needed the fault diagnosis pxpert systems that can detect and diagnose the fault diagnosis expert systems that can detect and diagnose the faults of some processes and give and advice to the operator in the event of process faults. We present the Time-Delay Neural Network(TDNN) approach for on-line fautl diagnosis. The on-line fault diagnosis system finds the exact origin of the fault of which the symptom is propagated continuously with time. The proposed method has been applied to a pilot distillation column to show the merits and applicability of the TDNN.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.275-281
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• 2013

This space is for the of your study in English. If simple menu item changes or the addition of check items are necessary on GUI menu of existing test equipments for military facilities that are programmed by using RAD tools such as Visual C++, they should go through complex steps, such as numerous conducting steps, coding, flash design modification, recompiling and distribution. It is cumbersome process and waste much time. Also, on implementing them, it was worried about leaking secrets because a number of military security considerations were included. To solve such as the above problem, we proposed commercial RIA technologies and a COTS fault diagnostic knowledge-based system that implemented by the XML data design technique in this research. The proposed approach solves the problem of existing methods, reduced inspection time, and improved performance, usability, and maintainability.