• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.774-779
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• 2001

If rotating machinery have a fault, we can detect it using vibration or noise signals. However some maintenance engineers who doesn't have expert knowledge, needs the help of vibration experts for diagnosing rotating machinery. But qualified experts are rare, therefore we have been developed the case based reasoning (CBR) system which is able to manipulate the past experiences of vibration diagnosis experts. In the CBR system, the maintenance engineers can retrieve too information from previous cases which are most similar to new problem and they can solve new problem using solutions from the previous cases. In this paper, we propose a new method which is the case retrieval of CBR system using Petri net and we also applied it to diagnosis for electric motors as a practical problem.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.107-109
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• 1999

In the point of view protective relay, the selectivity and sensitivity is very important in its performance. The correct setting of the relay parameter is required. The verification of accurate setting for the protective relay is very difficult before a actual fault occurs. In this paper, we proposed the diagnosis expert system as a method to verify the correctness of the relay setting. The developed system proved effectiveness through the tests on the real systems.

• Journal of the Korean Society of Safety
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• v.7 no.2
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• pp.14-23
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• 1992

A methodology for developing an object description system based on functional-structure of chemical plant is proposed. A knowledge base for chemical plant fault diagnosis is also organized in a generic fashion using the heuristic knowledge of human operators. A plant can be seen as a hierarchical set of subsystems. Each subsystem is called a SCOPE. The state of the plant and the behavior of each subsystem is managed by the SCOPES. A computer-based system based on thls methodology and knowledge base has been developed and applied to the subprocess of ethylene plant to evaluate the effectiveness of the methodology.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.5
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• pp.450-457
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• 2004

In this research, the automatic synthesis of knowledge models is proposed. which are the basis of the methods using qualitative models adapted widely in fault diagnosis and hazard evaluation of chemical processes. To provide an easy and fast way to construct accurate causal model of the target process, the Role-Behavior modeling method is developed to represent the knowledge of modularized process units. In this modeling method, Fault-Behavior model and Structure-Role model present the relationship of the internal behaviors and faults in the process units and the relationship between process units respectively. Through the multiple modeling techniques, the knowledge is separated into what is independent of process and dependent on process to provide the extensibility and portability in model building, and possibility in the automatic synthesis. By taking advantage of the Role-Behavior Model, an algorithm is proposed to synthesize the plant-wide causal model, Fault-Causality Graph (FCG) from specific Fault-Behavior models of the each unit process, which are derived from generic Fault-Behavior models and Structure-Role model. To validate the proposed modeling method and algorithm, a system for building FCG model is developed on G2, an expert system development tool. Case study such as CSTR with recycle using the developed system showed that the proposed method and algorithm were remarkably effective in synthesizing the causal knowledge models for diagnosis of chemical processes.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.27-34
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• 2010

Recently, lots of internet service companies provide on-line health diagnosis systems. But general persons not having expert knowledge are difficult to use, because most of the health diagnosis systems present prescriptions or dietetic treatments for diseases based on western medicine. In this paper, a self health diagnosis system of oriental medicine coinciding with physical characteristics of Korean using fuzzy ART algorithm, is proposed. In the proposed system, three high rank of diseases having high similarity values are derived by comparing symptoms presented by a user with learned symptoms of specific diseases based on treatment records using neural networks. And also the proposed system shows overall symptoms and folk remedies for the three high rank of diseases. Database on diseases and symptoms is built by several oriental medicine books and then verified by a medical specialist of oriental medicine. The proposed self health diagnosis system of oriental medicine showed better performance than conventional health diagnosis systems by means of learning diseases and symptoms using treatment records.

• Journal of Bio-Environment Control
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• v.3 no.1
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• pp.28-35
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• 1994

An expert system, Watermelon Disease Expert System (WDES), was developed in this study using HCLIPS which allows input and output in Korean. WDES could diagnose 8 diseases and 1 physiological disorder frequently occurring on watermelons grown in greenhouses. The knowledge base of WDES consisted of 35 ‘IF -THEN ’rules and the forward chaining was used to make inferences. Help menu providing information on the nature of questions in text and image forms was included for users to answer questions without difficulty. Watermelon growers and researchers have validated the system and proved possibility of its practical use. In order to facilitate the practical use of WDES by watermelon growers, the knowledge base of WDES needs to be improved by including more detailed information on various diseases and disorders and restructuring rule bases.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.1
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1186-1207
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• 2024

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults. Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research. Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis. However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis. Models with interpretability can enhance decision-makers' trust in the output results. However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results. Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper. Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy. By incorporating interpretability constraints into the model, interpretability is effectively ensured. Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.4
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• pp.29-35
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• 2003

The rotating machineries always generate harmonic frequencies of their own rotating speed, and increment of vibration amplitude affects to the equipments which connected to the vibrational source and causes industrial calamities. The life cycle of equipments can be extended and damages to the human beings could be prevented by identifying the cause of malfunctions through prediction of the increment of vibration and records of vibrational history. In this study, therefore, diagnostic expert algorithm for the centrifugal pump is developed by integrating fuzzy inference method and signal processing techniques. And the validity of the developed diagnostic system is examined via various computer simulations.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.1499-1504
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• 2007

This dissertation explains the construction of the expert system which can manage diagnosis and administration of trouble using circuit diagram through connection between BOM and ability term and makes up for problem which have designed RIMS with name of parts. Also this dissertation explains that existing BOM has no regard organic relation of parts and functional and systematic appearance of the flow of electricity or air. Construction of expert system and "Be enlivened BOM" with flowing electricity and air can solve problems about overlapping parts carrying out other functions with the same name of parts following to simple and physical BOM　management and having mistaken trouble of the end caused by basis for trouble of the end.