• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.92-102
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• 2003

To detect electrical faults of a TFT (Thin Film Transistor) panel for the LCD (Liquid Crystal Display), techniques of converting electric field to an image are used One of them is the PDLC (polymer-dispersed liquid crystal) modulator which changes light transmittance under electric field. The advantage of PDLC modulator in the electric field detection is that it can be used without physically contacting the TFT panel surface. Specific pattern signals are applied to the data and gate electrodes of the panel to charge the pixel electrodes and the image sensor detects the change of transmittance of PDLC positioned in proximity distance above the pixel electrodes. The image represents the status of electric field reflected on the PDLC so that the characteristic of the PDLC itself plays an important role to accurately quantify the defects of TFT panel. In this paper, the image of the PDLC modulator caused by the change of electric field of the pixel electrodes on the TFT panel is acquired and how the characteristics of PDLC reflect the change of electric field to the image is analyzed. When the holding time of PDLC is short, better contrast of electric field image can be obtained by changing the instance of applying the driving voltage to the PDLC.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.55-61
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• 2010

In this paper, we proposed selective extraction method of frequency information and PCA-ELM based diagnosis system for three-phase induction motors. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by variance As the next step, feature extraction is performed by principal component analysis (PCA). Finally, we used the classifier based on Extreme Learning Machine (ELM) with fast learning procedure. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.288-294
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• 2013

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.4
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• pp.255-260
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• 2018

In recent years, increasing electricity use has led to considerable interest in green energy. In order to effectively supply, cut off, and operate an electric power system, many electric power facilities such as gas insulation switch (GIS), cable, and large substation facilities with higher densities are being developed to meet demand. However, because of the increased use of aging electric power facilities, safety problems are emerging. Electromagnetic wave and leakage current detection are mainly used as sensing methods to detect live-line partial discharges. Although electromagnetic sensors are excellent at providing an initial diagnosis and very reliable, it is difficult to precisely determine the fault point, while leakage current sensors require a connection to the ground line and are very vulnerable to line noise. The partial discharge characteristic in particular is accompanied by statistical irregularity, and it has been reported that proper statistical processing of data is very important. Therefore, in this paper, we present the results of analyzing ${\Phi}-q-n$ cluster distributions of partial discharge characteristics by using K-means clustering to develop an expert partial discharge diagnosis system generated in a GIS facility.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.10
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• pp.102-111
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• 2021

Maintainability is a major design parameter that includes availability as well as reliability in a RAM (reliability, availability, maintainability) analysis, and is an index that must be considered when developing a system. There is a lack of awareness of the importance of predicting and analyzing maintainability; therefore, it is dependent on past-experience data. To improve the utilization rate, maintainability must be managed as a key indicator to meet the user's requirements for failure maintenance time and to reduce life-cycle costs. To improve the maintainability-prediction accuracy in the detailed design stage, we present a maintainability-prediction method that applies Method B of the Military Standardization Handbook (MIL-HDBK-472) Procedure V, as well as a Korean maintainability-prediction software package that reflects the system complexity.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.833-852
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• 2019

In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.

• Tribology and Lubricants
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• v.36 no.6
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• pp.297-306
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• 2020

This paper reviews studies on the types and functions of oil sensors used for machine condition monitoring. Machine condition monitoring is essential for maintaining the reliability of machines and can help avoid catastrophic failures while ensuring the safety and longevity of operation. Machine condition monitoring involves several components, such as compliance monitoring, structural monitoring, thermography, non-destructive testing, and noise and vibration monitoring. Real-time monitoring with oil analysis is also utilized in various industries, such as manufacturing, aerospace, and power plants. The three main methods of oil analysis are off-line, in-line, and on-line techniques. The on-line method is the most popular among these three because it reduces human error during oil sampling, prevents incipient machine failure, reduces the total maintenance cost, and does not need complicated setup or skilled analysts. This method has two advantages over the other two monitoring methods. First, fault conditions can be noticed at the early stages via detection of wear particles using wear particle sensors; therefore, it provides early warning in the failure process. Second, it is convenient and effective for diagnosing data regardless of the measurement time. Real-time condition monitoring with oil analysis uses various oil sensors to diagnose the machine and oil statuses; further, integrated oil sensors can be used to measure several properties simultaneously.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1305-1307
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• 2022

자동차 부품의 결함은 시스템 전체의 성능 저하 및 인적 물적 손실이 발생할 수 있으므로 생산라인에서의 불량 검출은 매우 중요하다. 따라서 정확하고 균일한 결과의 불량 검출을 위해 딥러닝 기반의 고장 진단 시스템이 다양하게 연구되고 있다. 하지만 제조현장에서는 정상 샘플보다 비정상 샘플의 발생 빈도가 현저히 낮다. 이는 학습 데이터의 클래스 불균형 문제로 이어지게 되고, 이러한 불균형 문제는 고장을 판별하는 분류 모델의 성능에 영향을 끼치게 된다. 이에 본 연구에서는 모터의 동작음으로부터 불량 모터를 판별하는 불량 검출 시스템 설계를 위한 데이터 불균형 해결 방법을 제안한다. 자동차 사이드 미러 모터의 동작음을 학습 및 테스트를 위한 데이터 셋으로 사용하였으며 손실함수 계산 시 학습 데이터 셋의 클래스별 샘플 수 가 반영되는 label-distribution-aware margin(LDAM) loss 와 Inception, ResNet, DenseNet 신경망 모델의 비교 분석을 통해 불균형 데이터를 처리할 수 있는 가능성을 보여주었다.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.523-523
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• 2012

As the wafer geometric requirements continuously complicated and minutes in tens of nanometers, the expectation of real-time add-on sensors for in-situ plasma process monitoring is rapidly increasing. Various industry applications, utilizing plasma impedance monitor (PIM) and optical emission spectroscopy (OES), on etch end point detection, etch chemistry investigation, health monitoring, fault detection and classification, and advanced process control are good examples. However, process monitoring in semiconductor manufacturing industry requires non-invasiveness. The hypothesis behind the optical monitoring of plasma induced ion current is for the monitoring of plasma induced charging damage in non-invasive optical way. In plasma dielectric via etching, the bombardment of reactive ions on exposed conductor patterns may induce electrical current. Induced electrical charge can further flow down to device level, and accumulated charges in the consecutive plasma processes during back-end metallization can create plasma induced charging damage to shift the threshold voltage of device. As a preliminary research for the hypothesis, we performed two phases experiment to measure the plasma induced current in etch environmental condition. We fabricated electrical test circuits to convert induced current to flickering frequency of LED output, and the flickering frequency was measured by high speed optical plasma monitoring system (OPMS) in 10 kHz. Current-frequency calibration was done in offline by applying stepwise current increase while LED flickering was measured. Once the performance of the test circuits was evaluated, a metal pad for collecting ion bombardment during plasma etch condition was placed inside etch chamber, and the LED output frequency was measured in real-time. It was successful to acquire high speed optical emission data acquisition in 10 kHz. Offline measurement with the test circuitry was satisfactory, and we are continuously investigating the potential of real-time in-situ plasma induce current measurement via OPMS.

• The KIPS Transactions:PartA
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• v.16A no.1
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• pp.17-26
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• 2009

Large-scale wireless sensor network are expected to play an increasingly important role for the data collection in harmful area. However, the physical fragility of sensor node makes reliable routing in harmful area a challenging problem. Since several sensor nodes in harmful area could be damaged all at once, the network should have the availability to recover routing from node failures in large area. Many routing protocols take accounts of failure recovery of single node but it is very hard these protocols to recover routing from large scale failures. In this paper, we propose a routing protocol, which we refer to as LSFA, to recover network fast from failures in large area. LSFA detects the failure by counting the packet loss from parent node and in case of failure detection LSFAdecreases the routing interval to notify the failure to the neighbor nodes. Our experimental results indicate clearly that LSFA could recover large area failures fast with less packets than previous protocols.