• 수산해양교육연구
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• 제22권4호
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• pp.553-565
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• 2010

The paper aims to evaluate the quality of fisheries census statistic and to provide some desirable directions and improvements for the future fisheries census, conducted by the Government. For the quality diagnosis of fisheries census statistic, specific processes of fisheries census and statistical qualities of each dimension are surveyed and evaluated by a Government's practician, two external examiners and a research group. Results show that census design, data analysis and quality control are evaluated relatively low in specific processes, and accessibility and comparability are evaluated relatively lower than relevance, accuracy, timeliness and consistency in statistical qualities. For minimizing the sampling errors, the probability proportion method should be employed in sampling methods from currently simple sampling method. In addition, fisheries census statistic is desirable to include and compare with those of different countries for consumer oriented data system.

• Journal of Magnetics
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• 제21권2호
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• pp.249-254
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• 2016

Recently, electric vehicles have got significant attention because it is more eco-friendly and efficient than internal combustion engine vehicles. Instead of an internal combustion engine, the electric vehicle has a motor for propulsion. The permanent magnet synchronous motor which has permanent magnet instead of field winding in the rotor has especially higher efficiency and power density than other types of motor. When the irreversible demagnetization is occurred, drivers are exposed to high risk of accident by the fault operation of motor. Therefore, the irreversible demagnetization of permanent magnet should be detected to reduce the risk of accident. In this study, the demagnetization diagnosis method based on the result of locked rotor test is proposed. Based on short measurement time, the proposed diagnosis method aims to detect the demagnetization fault when an electric vehicle is at a complete standstill. The proposed method is verified through the finite element analysis.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.597-608
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• 1998

A diagnosis system that provides early warnings regarding machine malfunction is very important for rolling mill so as to avoid great losses resulting from unexpected shutdown of the production line. But it is very difficult to provide early warnings in rolling mill. Because dynamics of rolling mill is non-linear. This paper proposes a new method for diagnosis of rolling mill using wavelet to solve this problem. Proposed method that measures the vibration signals of rolling mill on-line and analyze it using wavelet to acquire pattern datas. And we design a nero-fuzzy model that diagnose a rolling mill using this data. Validity of the new method is asserted by numerical simulation.

• KIEE International Transactions on Power Engineering
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• 제5A권1호
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• pp.40-45
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• 2005

Substation facilities have become extremely large and complex parts of electric power systems. The development of condition monitoring and diagnosis techniques has been a very significant factor in the improvement of substation transformer security. This paper presents a method to analyze the cause, the degree, and the aging process power transformers by the Self Organizing Map (SOM) method. Dissolved gas data were non-linearly transformed by the sigmoid function in SOM that works much the same way as the human decision making process. The potential for failure and the degree of aging of normal transformers are identified by using the proposed quantitative criterion. Furthermore, transformer aging is monitored by the proposed criterion for a set of transformers. To demonstrate the validity of the proposed method, a case study is performed and its results are presented.

• 대한한의진단학회지
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• 제11권2호
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• pp.68-83
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• 2007

Background and Purpose: According to chaos theory, irregular signals of electroencephalogram can interpretated by nonlinear method. Chaotic nonlinear dynamics in EEG can be studied by calculating the correlation dimension. The aim of this study is to analyze EEG by correlation dimension and do Correlation Analysis of correlation dimension and K-POMS factors score. Method: EEG raw data were measured during 15 minutes and choosed 40 seconds. We calculated correlation dimension and used surrogate data method for checking nonlinear data. After then do correlation analysis. Result and Conclusion: Correlation dimension of channel 6, channel 7 and channel 8 are showed significant correlation with vigor factor.

• KEPCO Journal on Electric Power and Energy
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• 제7권2호
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• pp.263-268
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• 2021

This paper statistically analyzes the time required for each failure cause and describes a diagnostic method for 159 reports of failure analysis of MV cables that occurred in the distribution system of KEPCO over the past 18 years. In addition, the manufacturer's failure rate compared to 100C-km was calculated using 381 cases of MV cable deterioration failure between 2008 and 2020. It is hoped that this paper will help those in charge of maintaining underground facilities at the business office to use the failure rate to prioritize facility diagnosis.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2001년도 춘계학술대회 논문집 센서 박막재료
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• pp.66-70
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• 2001

The partial discharge testing is widely used in diagnostic measuring technology because it gives low stress to power equipment which is undertaken tests. Therefore it is very useful method compare to previous destructive methods and effective diagnosis method in power system that requires on-line/on-site diagnosis. But partial discharges have very complex characteristics of discharge pattern, so it is required continuous research to development of precise analysis method. In recent, the study of partial discharge is carrying out discover of initial defect of power equipment through condition diagnosis and system development of degradation diagnosis using HFPD(High Frequency Partial Discharge) detection. In this study, simulated system is manufactured and HFPD occurred from those simulator is measured with broad-band antenna in real time, the degradation grade of system is analyzed through produced patterns in simulated target according to the AI/statistics processing.

• 한국안전학회지
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• 제31권4호
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• pp.35-41
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• 2016

In order to guarantee the process safety and prevent accidents, the deviations from normal operating conditions should be monitored and their root causes have to be identified as soon as possible. The statistical theories-based method among various fault diagnosis methods has been gaining popularity, due to simplicity and quickness. However, according to fault magnitudes, the scalar value generated by statistical methods can be changed and this point can lead to produce wrong information. To solve this difficulty, this work employs PCA (Principal Component Analysis) based method with qualitative information. In the case study of our previous study, the number of assumed faults is much smaller than that of process variables. In the case study of this study, the number of predefined faults is 19, while that of process variables is 6. It means that a fault diagnosis becomes more difficult and it is really hard to isolate a single fault with a small number of variables. The PCA model is constructed under normal operation data in order to get a loading vector and the data set of assumed faulty conditions is applied with PCA model. The significant changes on PC (Principal Components) axes are monitored with CUSUM (Cumulative Sum Control Chart) and recorded to make the information, which can be used to identify the types of fault.

• 한국정보통신학회논문지
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• 제4권5호
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• pp.951-961
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• 2000

압연기의 기계적인 이상을 사전에 알아내는 압연기 베어링 고장-진단 시스템은 예측하지 못하는 압연 공정의 중단으로 인하여 발생하는 큰 피해를 사전에 막기 위해서 매우 중요한 시스템이다. 그러나 압연기의 동적 거동은 비선형 특성이 매우 강하기 때문에 압연기에서 사전에 고장 예측 정보를 제공하는 것은 매우 어렵다. 본 논문에서는 웨이브렛을 이용한 압연기의 고장 진단 방법을 제안하였으며 제안된 방법은 온라인으로 압연기에서 진동 신호를 실시간으로 측정하여 웨이브렛을 이용하여 패턴을 분석하고 분석된 결과로부터 고장 특징 정보를 얻었다. 얻어진 데이터를 이용하여 압연기 베어링을 진단하는 뉴로 퍼지 모델을 설계하고 수치적인 시뮬레이션을 통하여 그 타당성을 입증하였다.

• 설비공학논문집
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• 제17권1호
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• pp.47-55
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• 2005

An experimental study has been peformed to develop a scheme for fault detection and diagnosis(FDD) in a vapor compression refrigeration system. This study is to analyze fault effect on the system performance and to find efficient diagnosis rules for easy determination of abnormal system operation. The refrigeration system was operated with a variable speed compressor to modulate cooling capacity. The FDD system was designed to consider transient load conditions. Four major faults were considered, and each fault was detected over wide operating load range by separating the system response to the load change. Rule-based method was used to diagnose and classify the system faults. From the experimental results, COP degradation due to the faults in a variable speed system is severer than that in a constant speed system. The method developed in this study can be used in the fault detection of refrigeration systems with a variable speed compressor.