• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.42-48
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• 2003

In general many engineers can diagnose the fault condition using the abnormal ones among data monitored from a diesel engine, but they don't need the system modelling or identification for the work. They check the abnormal data and the relationship and then catch the fault condition of the engine. This paper proposes the construction of a fault diagnosis engine through malfunction data gained from the data fault detection system of neural networks for diesel generator engine, and the rule inference method to induce the rule for fuzzy inference from the malfunction data of diesel engine like a site engineer with a fuzzy system. The proposed fault diagnosis system is constructed in the sense of the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HMH). The system is concerned with the rule reduction method of knowledge base for related data among the various interactive data.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.115-131
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• 1998

Customer service process is one of the most important processes in today's competitive business environment. Among the various activities of customer service process, equipment malfunction diagnosis activity should be performed fast and accurately. When a customer calls the service center and reports the observed symptoms, he/she describes them in layman's terms. Therefore, the customer-reported symptoms have not been considered helpful information for service representatives. However, in order to perform diagnosis activity fast and accurately, we need to make use of the customer-reported symptoms actively. In this research, we developed three systems called R-EMD (Rule-based Equipment Malfunction Diagnostic system), C-EMD (Case-based Equipment Malfunction Diagnostic system) and R&C-EMD (Rule & Case-based Equipment Malfunction Diagnostic system), each of which diagnoses equipment malfunctions using the customer-reported symptoms. R&C-EMD is a hybrid system that utilizes both rule-based and case-based technologies. The diagnosis rules used in R&C-EMD and R-EMD were not acquired from service manuals or interviews with service representatives. Rater, we extracted them directly from the past diagnosis cases based on symptoms' frequencies. By this way, we were able to overcome the knowledge acquisition bottleneck. Using the real 100 malfunction diagnosis cases, we evaluated the performances of R&C-EMC, R-EMD and C-EMD in terms of speed and accuracy. In diagnosis time, R&C-EMD took longer than R-EMD and shorter than C-EMD. However, R&C-EMC was the best in accuracy.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.233-238
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• 2002

This paper propose the construction of fault diagnosis engine for diesel generator engine and rule inference method to induce rule for fuzzy inference from the monitored data of diesel engine. The proposed fault diagnosis system is constructed the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HME), It is Proposed the rule reduction method of knowledge base for concerning data among the various analog data.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.4
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• pp.493-500
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• 2002

The operational data of diesel generator engine is two kinds of data. One is interactive the other is non interactive. We can find the fault information from interactive data measured for every sampling time when the changing rate, direction and status of data are investigated in comparition with those of normal status to diagnose the fault of combustion system. The various data values of combustion system for diesel engine are not proportional to load condition. The criterion to decide the level of data value is not absolute but relative to relational data. This study proposes to compose malfunction diagnosis engine using neural networks to decide that level of data value is out of normal status with the data collected from generator engine of the ship using the commercial data mining tool. This paper investigates the real ship's operational data of diesel generator engine and confirms usefulness of fault detecting through simulations for fault detecting.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.245-250
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• 2002

The operational data of diesel generator engine is two kind of discrete signal and analog signal. We can find the fault information from analog data measured for every sampling time if it is invested the changing rate or direction of data. This paper propose the Malfunction Diagnosis Engine(MDE) using the commercial data mining tool and show the data Process and fault finding method with the data collected from generator engine of the ship.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.97-112
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• 2000

The basic idea of case-based reasoning is to solve a new problem using the previous problem-solving experiences. In this research we develop a case-based reasoning system for equipment malfunction diagnosis. We first divide the case base into clusters using the case-based clustering technique. Then we develop an appropriate case-based diagnostic system for each cluster. In other words for individual cluster a different case-based diagnostic system which uses different weights for attributes is developed. As a result multiple case-based reasoning system are operating to solve a diagnostic problem. In comparison to the performance of the single case-based reasoning system our system reduces the computation time by 50% and increases the accuracy by 5% point.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.168-176
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• 1996

A diagnosis system which can monitor tool breakage and chipping in real time was developed using a DSP(Digital Signal Processor) board in face milling operation. AR modelling and band energy method were used to extract the feature of tool states from cutting force signals. Artificial neural network embedded on DSP board discriminates different patterns from features got after signal processing. The features extracted from AR modelling are more accurate for the malfunction of a process than those from band energy method, even though the computing speed of the former is slow. From the processed features, we can construct the real time diagnosis system which monitors malfunction by using a DSP board having a parallel processing capability.

• Journal of Korean Neurosurgical Society
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• v.66 no.2
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• pp.162-171
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• 2023

Objective : The goal of this study was to analyze the clinical outcomes of endoscopic third ventriculostomy (ETV) and endoscopic septostomy when shunt malfunction occurs in a patient who has previously undergone placement of a ventriculoperitoneal shunt. Methods : From 2001 to 2020 at Seoul National University Children's Hospital, patients who underwent ETV or endoscopic septostomy for shunt malfunction were retrospectively analyzed. Initial diagnosis (etiology of hydrocephalus), age at first shunt insertion, age at endoscopic procedure, magnetic resonance or computed tomography image, subsequent shunting data, and follow-up period were included. Results : Thirty-six patients were included in this retrospective study. Twenty-nine patients, 18 males and 11 females, with shunt malfunction underwent ETV. At the time of shunting, the age ranged from 1 day to 15.4 years (mean, 2.4 years). The mean age at the time of ETV was 13.1 years (range, 0.7 to 29.6 years). Nineteen patients remained shunt revision free. The 5-year shunt revision-free survival rate was 69% (95% confidence interval [CI], 0.54-0.88). Seven patients, three males and four females, with shunt malfunction underwent endoscopic septostomy. At the time of shunting, the age ranged from 0.2 to 12 years (mean, 3.9 years). The mean age at the time of endoscopic septostomy was 11.9 years (range, 0.5 to 29.5 years). Four patients remained free of shunt revision or addition. The 5-year shunt revision-free survival rate was 57% (95% CI, 0.3-1.0). There were no complications associated with the endoscopic procedures. Conclusion : The results of our study demonstrate that ETV or endoscopic septostomy can be effective and safe in patients with shunt malfunction.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.2056-2057
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• 2007

Recently we installed inverter system in $ind{\acute{u}}ction-m{\acute{o}}tor$ for the energy saving. But inverter system generates harmonics, and it causes a lot of issues. malfunction of meter, damage of instrument, overheating of motor. In this study, you can see how to deal with harmonics of inverter system by diagnosis of the power quality.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.27 no.2
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• pp.1-13
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• 2014

Objective : The purpose of this study is to understand conspicuous features of geroderma with visceral manifestation theory(臟象論). Methods : We categorized skin aging into wrinkles, hyperpigmentation, dry skin and face flush. After investigating the reason, histological changes and mechanism of each classification in western medicine, we interpreted them according to the malfunction of five viscera(五臟) in Korean medicine. Result : The results are as follows. 1. Pathologic change of dermis and subcutaneous fat makes wrinkles. We consider wrinkles as the malfunction of the spleen(脾). 2. Irregular synthesis and disproportion of melanin makes hyperpigmentation. We consider hyperpigmentation as the malfunction of the liver(肝). 3. Dry skin is attributed to a subtle disorder of epidermal maturation. We consider dry skin as the malfunction of the lung(肺). 4. Facial flush is detected in rosacea and menopausal hot flush, which are both related with blood vessel abnormality. We consider facial flush as the malfunction of the heart(心) Conclusion : We interpreted the pathologic changes and mechanism of skin aging in western medicine as the decrease of five viscera(五臟) in visceral manifestation theory(臟象論) of Korean medicine. Further studies are needed to apply these hypothesis to clinical diagnosis and treatment.