• Journal of Advanced Marine Engineering and Technology
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• v.38 no.6
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• pp.744-750
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• 2014

The monitoring and abnormality warning of marine diesel engine are important to take appropriate responses for safety navigation. If maintenance engineers do not take appropriate response because of diagnosis mistakes, it will occur a nasty accident. Therefore, we need integrated monitoring and diagnosis system for supporting a diagnosis objectively. In this paper, we analyze time-series data which measured by real-time, monitor the changing of conditions and trends of the analyzed data. Furthermore, we design and implement a monitoring and diagnosis system for objective supporting of real-time diagnosis. When the integrated monitoring and diagnosis system is adopted, it can help to improve stability of marine diesel engine by providing abnormality warning alarm with appropriate responses.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.1
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• pp.71-85
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• 1994

Overhead cranes which carry heavy items in construction or production areas consist of structure and electric modules. More than 80% of breakdowns bring out of the electric module. As operators do not know all about cranes, it sometimes takes much time to repair the cranes. In order to resolve this problem, the expert system which can diagnose causes of faults and give instructions for repair to operators, has been developed. The scope of the paper is limited to the electric module. First of all, analyzing symptoms and causes, we have developed a rule base with the expert system shell, EXSYS. Furthermore, for the facility maintenance including repair instructions against the causes, the instruction data base was developed with FOXPRO. On the other hand, for the help of user's understanding the fault causes, the graphic animation module which shows malfunctioning component ports or motions in 3D was developed with the graphic software, TOPAS VGA.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1039-1041
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• 1998

This paper presents a practical fuzzy expert system to diagnose various faults occurred in local power systems. This integrated system can diagnose all faults occurred in a transmission network and substations. In this paper. the fuzzy reasoning of the diagnostic process is discussed in detail. The discrimination of false operations and non-operations of protective devices as well as the fault identification scheme are also analyzed together with the fuzzy inference process.

• IE interfaces
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• v.3 no.2
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• pp.1-11
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• 1990

In this study, we will present a knowledge-based consulting system, called THYCONS, for diagnosing thyroid disorders. It has been developed to make the knowledge and expertise of the human expert more widely available, therefore achieving a high-quality diagnosis. Frames will be used to represent the medical knowledge of thyroid disorders, and several rules are attached in each slot of a frame. The uncertainty of diagnostic processes is manipulated by the subjective Bayesian method under the assumption that the pieces of evidence are conditionally independent. Searching for the group of diagnostic tests to be carried out and their optimum sequences will be established in order to infer a more correct diagnosis on the basis of maximum information gain with cost and time restrictions. Additionally. differential diagnosis will be carried out based on the information gained.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• Journal of Digital Convergence
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• v.4 no.2
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• pp.143-151
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• 2006

The purpose of this paper is to make a design and implementation of a diagnosis system of learning misconception of students who learn 'be' verb in the English language by using fuzzy theory. In this system, a fuzzy cognitive map exposes the fact that students' perception and misunderstanding about 'the English' language have an intertwined relationship, and diagnoses causes of misconceptions of students by using fuzzy memory associative memory. It suggests that since most existing systems of rule based expert system have had several limitations, this system will be applied to diagnose learners' misconception of learning in varieties of education areas.

• 한국디지털정책학회:학술대회논문집
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• 2006.06a
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• pp.451-459
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• 2006

The purpose of this paper is to make a design and implementation of a diagnosis system of learning misconception of students who learn 'be' verb in the English language by using fuzzy theory. In this system, a fuzzy cognitive map exposes the fact that students' perception and misunderstanding about 'the English' language have an intertwined relationship, and diagnoses causes of misconceptions of students by using fuzzy memory associative memory. It suggests that since most existing systems of rule based expert system have had several limitations, this system will be applied to diagnose learners' misconception of learning in varieties of education areas.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.332-336
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• 1997

To order to remain competitive, a manufacturing company need to maintain the optimal condition of its manufacturing system. Machine tools as an important element of a manufacturing system comprises complex mechanical as well as electronic components. So, diagnosing the troubles of machine tools is tricky process which requires a lot of experience and knowledge. Since providing machine tool users with necessary serices at the right time is very difficult,a remote diagnosis system is to be regarded as a good alternative, with which users can diagnose and fix the machine troubles. This paper presents a method to implement a remote machine tool diagnosis system using the world wide web technology and backward reasoning expert system.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.215-222
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• 2001

The task for chromosome analysis and diagnosis by experienced cytogenetists are being concerned as repetitive, time consuming job and expensive. FOr that reason, chromosome analysis system based on knowledge base for CAI had been established to be able to analyze chromosomes and obtain necessary advises from the knowledge base instead of human experts. That s 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 chromosome 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 complete system 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.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.125-127
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• 2000

Multiple faults are hard to diagnose correctly because the operation of circuit breakers tripped by former fault changes the topology of power systems. The information including operating time of actuated relays and tripped circuit breakers is used for considering changes of the network topology in fault section diagnosis. This paper presents a method for fault section diagnosis using a set of matrices which represent changes of the network topology due to operation of circuit breakers. The proposed method uses fuzzy relation to cope with the unavoidable uncertainties imposed on fault section diagnosis of power systems. The inference executed by the proposed matrices provides the fault section candidates in the form of a matrix made up of the degree of membership. Experimental studies for real power systems reveal usefulness of the proposed technique to diagnose multiple faults.