• 대한기계학회논문집A
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• 제20권5호
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• pp.1411-1418
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• 1996

Large scale plants are equipped with a number of the rotating machineries which ocuupy important positions in the plant system. Therefore, the most important one is a vibraiton diagnostic thchnology which can detect quickly any abnormal symptom of operating malfunction and guve operational and inspection guides adequately. A new diagnosis method is developed in this paper, in which the fuzzy set theory is introduced to diagnose the defects of ratating machinery. The selection of memgership function and the fuzzy operation model are discussed in datail here. The systme is sucessfully used for various defacts diagnosis of rotating machinery. The result indicate that realixtic application can be builtusing this approach.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.790-795
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• 1994

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item, component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introducd to Hierarchical Artificial Neural Network(HANN) for this purpose. HANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification,forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trainined by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing HANN with multitasking and message transfer between processes in SUN workstation. We tested HANN in reactor system.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2008년도 하계학술발표대회 논문집
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• pp.36-41
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• 2008

Fault detection and diagnosis(FDD) system is beneficial in equipment management by providing the operator with tools which can help find out a failure of the system. An experimental study has been performed on fault detection and diagnosis method for a water chiller. Bayes classifier, which is one of classical pattern classifiers, is adopted in deciding whether fault occurred or not. FDD algorithm can detect refrigerant leak failure, when 20% amount of charged refrigerant for normal operation leaks from the water chiller. The refrigerant leak failure caused COP reduction by 6.7% compared with normal operation performance. When two kinds of faults, such as a decrease in the mass flow rate of cooling water and temperature sensor fault of cooling water inlet, are detected, COP is a little decreased by these faults.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.371-375
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• 2004

Fault diagnosis is one of the most studied problems in process engineering. Recently, great research interest has been devoted to approaches that use classification methods to detect faults. This paper presents an application of a newly developed classification method - support vector machines - for fault diagnosis in an industrial case. A real set of operation data of a motor pump was used to train and test the support vector machines. The experiment results show that the support vector machines give higher correct detection rate of faults in comparison to rule-based diagnostics. In addition, the studied method can work with fewer training instances, what is important for online diagnostics.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.271-290
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• 2013

The acceleration information is significant for the structural health monitoring, which is the basic measurement to identify structural dynamic characteristics and structural vibration. The efficiency of the accelerometer is subsequently important for the structural health monitoring. In this paper, the distance measure matrix and the support level matrix are constructed firstly and the synthesized support level and the fusion method are given subsequently. Furthermore, the synthesized support level can be served as the determination for diagnosis on accelerometers, while the consensus data fusion method can be used to recover the acceleration information in frequency domain. The acceleration acquisition measurements from the accelerometers located on the real structure National Aquatics Center are used to be the basic simulation data here. By calculating two groups of accelerometers, the validation and stability of diagnosis and recovering on acceleration based on the data fusion are proofed in the paper.

• 한국안전학회지
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• 제29권4호
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• pp.73-77
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• 2014

In the field of fault diagnosis, the deviations from normal operating conditions are monitored to identify the type of faults and find their root causes. One of the most representative methods is the statistical approaches, due to a large amount of advantages. However, ambiguous diagnosis results can be generated according to fault magnitudes, even if the same fault occurs. To tackle this issue, this work proposes principal component analysis (PCA) based method with qualitative information. The PCA model is constructed under normal operation data and the residuals from faulty conditions are calculated. The significant changes of these residuals are recorded to make the information for identifying the types of fault. This model can be employed easily and the tasks for building are smaller than these of other common approaches. The efficacy of the proposed model is illustrated in Tennessee Eastman process.

• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3256-3263
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• 2021

The main steam line break accident is an essential initiating event of the pressurized water reactor. In present work, the fuzzy set theory and the signal-based fault detection method has been used to detect the occurrence and diagnosis of the location and break area for the small scale MSLB. The models are validated by the AP1000 accident simulator based on MAAP5. From the test results it can be seen that the proposed approach has a rapid and proper response on accident detection and location diagnosis. The method proposed to evaluate the break area shows good performances for small scale MSLB with the relative deviation within ±3%.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.131-136
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• 2018

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.

• 농촌지도와개발
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• 제21권4호
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• pp.1149-1189
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• 2014

현재 국내에서 많은 농가가 이용하는 경영진단방법 중 하나는 농촌진흥청 농업경영정보시스템 내의 농가경영컨설팅시스템에서 사용되는 경영표준진단표로, 진단결과는 DB화하여 농가경영컨설팅을 위한 자료로 활용되고 있다. 하지만 연도별 농가경영 진단결과 DB화 실적을 살펴보면 매년 실적이 조금씩 감소함을 알 수 있는데, 경영진단을 하는 농가 수의 감소는 농가가 경영진단을 통한 실질 효과가 기대에 미치지 못하였거나, 지속적으로 진단할 필요를 느끼지 못하는 등의 불만족 요인이 있음을 알 수 있다. 이러한 맥락에서 농가의 다양한 유형과 규모를 포괄하여 쓸 수 있는 컨설팅 기법의 개발과 실증사례의 발굴이 절실하다. 본 연구는 기존의 농가 중심의 농업경영체 경영진단 및 컨설팅 방법에서 부족한 요소들을 보완하고 기업형 농업경영체에도 적용이 가능하도록 경영전략 이론에서 사용되는 다중조직이론과 가치사슬모형을 적용한 경영진단 및 컨설팅기법을 소개한다. 다중조직이론 기반의 컨설팅 기법의 특징은 농업경영체의 경영전략은 크게 효율성추구와 효과성추구의 두 가지 방향이 있으며 경영체의 하부요소들이 경영 전략의 방향과 일치하는 방향으로 적합성(fit)을 맞추도록 하는 것이 특징이다. 즉, 경영체의 전략적 방향에 맞는 경영활동이 중요하며 이를 통해 한정된 자원의 분배를 최적화할 수 있게 된다. 제시된 컨설팅 기법은 농업현장의 경영체들에 실제로 컨설팅을 수행하여 현장실증한 사례를 보여줌으로써 컨설팅 기법에 대한 이해를 높일 수 있도록 하였다.

• Korean Journal of Otorhinolaryngology-Head and Neck Surgery
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• 제61권11호
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• pp.593-599
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• 2018

Background and Objectives Respiratory scoring guidelines for children and adults have been used for evaluating adolescents both in the 2007 and 2012 American Academy of Sleep Medicine (AASM) scoring manuals. We compared the scoring methods of polysomnography used in these scoring manuals, where pediatric and adult scoring rules were adopted for the diagnosis of sleep apnea in adolescents. Subjects and Method 106 Korean subjects aged between 13 and 18 years were enrolled. All subjects underwent overnight polysomnography in a sleep laboratory. Data were scored according to both pediatric and adult guidelines in the 2007 and 2012 AASM scoring manuals. Results Both pediatric and adult apnea hypopnea index (AHI) using the 2012 method were significantly higher than those using the 2007 method. The difference in AHI compared between pediatric and adult scores with the 2012 AASM scoring system was markedly decreased from that with the 2007 method. There was a significant discordance in sleep apnea diagnosis between pediatric and adult scoring rules in the 2012 method. Conclusion Both pediatric and adult rules were used for the diagnosis of adolescent sleep apnea in the 2012 method. However, there was significant discordance in the diagnosis between pediatric and adult scoring guidelines in the 2012 AASM manual, probably due to different cut-off values of AHI for the diagnosis of sleep apnea in pediatric (${\geq}1$) and adult (${\geq}5$) patients. Further studies are needed to determine a more reasonable cut-off value for the diagnosis of sleep apnea in adolescents.