• Journal of Industrial Technology
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• v.34
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• pp.67-70
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• 2014

In this paper, we propose a diagnosis algorithm for 25.8kV $N_2$ insulated Pad-mounted Switchgear in oder to improve reliability by preventing of fault in advance. The proposed algorithm can diagnose the problems of Pad-mounted Switchgear such as gas leakage and VI(Vacuum Interrupter) trouble (contact abrasion, coil aging etc.) by using pressure sensor, stroke sensor and coil current sensor.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.75-83
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• 2007

IEC code based decision nile have been widely applied to detect incipient faults in power transformers. However, this method has a drawback to achieve the diagnosis with accuracy without experienced experts. In order to resolve this problem, we propose an artificial diagnosis algorithm to detect faults of power transformers using Self-Organizing Feature Map(SOM). The proposed method has two stages such as model construction and diagnostic procedure. First, faulty model is constructed by feature maps obtained by unsupervised learning for training data. And then, diagnosis is performed by compare feature map with it obtained for test data. Also the proposed method usぉms the possibility and degree of aging as well as the fault occurred in transformer by clustering and distance measure schemes. To demonstrate the validity of proposed method, various experiments are unformed and their results are presented.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.8
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• pp.361-367
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• 2002

Many failures in high voltage equipment are preceded by partial discharge activity. In this paper deals with the application of the high frequency partial discharge measurement technique in motorette. HFPD measurement is very effective method to detect the PD occurred in motorette which is the called name of test specimen for accelerating test of stator winding[1] In this study, CT type HFPD sensor is used to detect the partial discharges and a measured HFPD pattern is analyzed by fractal mathematics. The neural network algorithm is used to pattern recognition and ageing diagnosis. As a result of this study, the fractal dimensions are increased along to applied voltage and HFPD pattern recognition using neural network shown excellent recognition rate. Also, the ageing diagnosis of motorette has been Possible.

• Proceedings of the KIEE Conference
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• 2004.11a
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• pp.251-254
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• 2004

The aging diagnosis technique using partial discharge detection method detects partial discharge signals cause of power equipment failuer and able to forecast the aging state of insulation system through analysis algorithm, in this paper accumulates HFPD signal during constant scheduled cycles to build HFPD pattern and then analyzes HFPD pattern using statistical parameters and ${\Delta}F$ pattern. The 3D pattern is composed of detected signal frequency, amplitude and repeated number and the FRPDA(frequency resolved partial discharge analysis) technique is used in 3D pattern construction. The ${\Delta}F$ pattern shows variation characteristics of amplitude gradient of consecutive HFPD signal Pulses and able to classify discharge types-internal discharge, surface discharge and coronal discharge etc. Fractal mathematics applied to ${\Delta}F$ pattern quantification and neural networks is used in aging diagnostic algorithm.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.297-305
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• 2014

Nowadays new water supply projects have been on the decline as the water-power constructions have saturated, which means that the existing power equipment have slowly aged and they require more efforts to maintain the system performance. An effective asset management method of power equipment has become a great necessity from both economical and technical aspects. To be balanced, the asset management should look into all three parts: management, engineering, and information. The purpose of this paper is to study a Risk-Based Maintenance (RBM) matrix method through the deterioration evaluation algorithm for an efficient reliability assessment of oil-immersed power transformers by considering both asset management and technical evaluation. Make use of this result, the equipment will be decided to be replace or repair otherwise on service.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.864-872
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• 2015

The purpose of this study is to design the algorithm, Predictive Service Component - PSC, for forecasting and judging obsolescence of solar system that is implemented based on the micro-inverter. PSC proposed in this study is suitable for monitoring of distributed power generation systems. It provides a diagnosis functionality to detect failures and anomaly events. It also can determine the aging of PV systems. The conclusion of this study shows the research and development of this kind of integrated system using PSC will be needed more and varied in the near future.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.113-118
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• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.

• Journal of the Optical Society of Korea
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• v.17 no.4
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• pp.344-349
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• 2013

This paper proposes a simulation algorithm to assess the gradual yellowing vision of the elderly, which refers to the predominance of yellowness in their vision due to aging of the ocular optic media. This algorithm employed the spectral transmittance property of a yellow filter to represent the color appearance perceived by elderly people with yellow vision, and modeled the changes in the color space through a spectrum change in light using the yellow filter effect. The spectral reflectivity data of 1269 Munsell matte color chips were used as reference data. Under the standard conditions of a D65 illuminant and a $10^{\circ}$ observer of 1964 CIE, the spectrum of the 1269 Munsell colors were processed through the yellow filter effect to simulate yellow vision. Various degrees of yellow vision were modeled according to the transmittance percentage of the yellow filter. The color differences before and after the yellow filter effect were calculated using the DE2000 formula, and the color pairs were selected based on the color difference function. These color pairs are distinguishable through normal vision, but the color difference diminishes as the degree of yellow vision increases. Assuming 80% of yellow vision effect, 17 color pairs out of $(1269{\times}1268)/2$ pairs were selected, and for the 90% of yellow vision effect, only 3 color pairs were selected. The result of this study can be utilized for the diagnosis system of gradual yellow vision, making various types of test charts with selected color pairs.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.161-166
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• 2021

Errors in the semiconductor process are generated by a change in the state of the equipment, and errors usually arise when the state of the equipment changes or when parts that make up the equipment have flaws. In this investigation, we anticipated that aging of the mass flow controller in the plasma enhanced chemical vapor deposition SiO2 thin film deposition method caused a minute flow rate shift. In seven cases, fourier transformation infrared film quality analysis of the deposited thin film was used to characterize normal and pathological processes. The plasma condition was monitored using optical emission spectrometry data as the flow rate changed during the procedure. Preprocessing was used to apply the collected OES data to the artificial immune system algorithm, which was then used to process diagnosis. Through comparisons between datasets, the learning algorithm compared classification accuracy and improved the method. It has been confirmed that data characterized as a normal process and abnormal processes with differing flow rates may be discriminated by themselves using the artificial immune system data mining method.