• Journal of Magnetics
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• v.21 no.2
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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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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• v.5A no.1
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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.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.11 no.2
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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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• v.7 no.2
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.05a
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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.

• Journal of the Korean Society of Safety
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• v.31 no.4
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.5
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• pp.951-961
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• 2000

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 transform(W) to solve this problem. Proposed method that measures the vibration signals of rolling mill on-line and analyze it using wavelet to acquire pattern data. And we design a fault-diagnosis system that diagnose a rolling mill using this data. Validity of the new method is asserted by real numerical data experiment.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.1
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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.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.732-737
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• 2006

The facility diagnosis technique of past subjective judged the technical expert and interpreted stably mainly. The facility the periodical civil official becomes indispensability consequently with the law and the system and objectivity inspection which relates with a this and development of diagnosis technique were necessary. Currently the evaluation elements which relate with an inspection and diagnosis technique depended to the subjectivity of each facility quality, data statistics, diagnosis know-how, various circumstance and the technical expert and the evaluation item came to decide. But the evaluation score distribution of marks of each item depended plentifully in data analysis result and experience. The past method is weak in logically because the objection theoretical background and basis are insufficiency. Consequently in order to apply a data analysis result and an experience logically, it used AHP(Analytic Hierarchy Process) method from this research. It is visible with the fact that the application result reconfirmation and complement process will be necessary the technique which is suitable in the multi facility management. Also it is expected with the fact that the application characteristic from the next relation technique will be excellent.