• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.25-38
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• 2019

This study is to develop a diagnostic model for the effective introduction of smart factories in the manufacturing industry, to diagnose SMEs that have difficulties in building their own smart factory compared to large enterprise, to identify the current level and to present directions for implementation. IT, AT, and OT experts diagnosed 18 SMEs using the "Smart Factory Capacity Diagnosis Tool" developed for smart factory level assessment of companies. They analyzed the results and assessed the level by smart factory diagnosis categories. Companies' smart factory diagnostic mean score is 322 out of 1000 points, between 1 level (check) and 2 level (monitoring). According to diagnosis category, Factory Field Basic, R&D, Production/Logistics/Quality Control, Supply Chain Management and Reference Information Standardization are high but Strategy, Facility Automation, Equipment Control, Data/Information System and Effect Analysis are low. There was little difference in smart factory level depending on whether IT system was built or not. Also, Companies with large sales amount were not necessarily advantageous to smart factories. This study will help SMEs who are interested in smart factory. In order to build smart factory, it is necessary to analyze the market trends, SW/ICT and establish a smart factory strategy suitable for the company considering the characteristics of industry and business environment.

• Journal of Digital Contents Society
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• v.9 no.4
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• pp.601-606
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• 2008

U-Healthcare is one of the major applications in ubiquitous sensor network. U-Healthcare has potential to become a critical service for the people who immediately require emergency ambulatory attention. This paper describes about the real time pulse monitoring and reporting system, consisting of two components: thus, the one is a reliable bio-sensor that continuously monitors the pulse information of the subject, and the other is the automatic transfer system that transmits pulse information to both his/her family and hospital care system through the Base Station. In the hospital, this bio-information can be used to treat the patient accordingly. I designed the pulse information monitored by a bio-sensor module that transfers the pulse information to both the Base Station and the central monitoring system through transmitting protocols such as Zigbee and TCP/IP, as well as designed the architecture of information packets for the corresponding protocols. Furthermore, the central monitoring system automatically parses the pulse information of the subject into the web database server, which can continuously provides the real time information and status of the subject via an internet browser to the clients who are family members of the subject and the authenticated medical care personnel as well.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1618-1628
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• 2011

The Train Diagnostic and Control System(TDCS) has been equipped on the modern Metro Vehicle, Locomotive and High Speed Train. The main purpose of this system is to support the identification of train status by real-time, the fast action against such failure events during revenue service and the fast convenient maintenance processes. Some of newest TCMS, a kind of control and monitoring system, has participated in the main control functions such as pantograph up and down, powering and braking command and so on. But these kind of control functions of the high speed train which has the operating speed over 300km/h are conducted by the train electrical logic circuits. The KTX-I and KTX-II - the local high speed train, are the typical example. The next generation TDCS for the ongoing project of distributed high speed train(HEMU) is designing with the target to increase main train control functions, to increase the reliability/avalibility and to increase the convenient driving. This paper introduces the overall configurations and functions of the new generation TDCS.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1765-1772
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• 2015

The integrity of the gas insulated switchgear (GIS) is vital to the safety of an entire power grid. However, there are some limitations on the techniques of detecting and diagnosing partial discharge (PD) induced by insulation defects in GIS. This paper proposes a joint electro-chemical detection method to resolve the problems of incomplete PD data source and also investigates a new unique fault diagnosis method to enhance the reliability of data processing. By employing ultra-high frequency method for online monitoring and the chemical method for detecting SF6 decomposition offline, the acquired data can form a more complete interpretation of PD signals. By utilizing DS evidence theory, the diagnostic results with tests on the four typical defects show the validity of the new fault diagnosis system. With higher accuracy and lower computation cost, the present research provides a promising way to make a more accurate decision in practical application.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.346.2-346
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• 2002

A diagnostic methodology, which utilizes only the remotely-measurable signals, has been requested to be developed in order to evaluate and monitor conditions of MOVs. It is proven that the stem thrust are the most important variables which provide the operability of MOVs. Therefore the stem thrust estimator was developed and validated, which estimates stem thrust by use of the motor torque. The motor torque is calculated using electrical signals which can be measured in Motor Control Center(MCC). (omitted)

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.2008-2009
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• 2007

The paper considers the relation between on-line monitoring and diagnostics on the one hand and high-voltage (HV) withstand and partial discharge (PD) on-site testing on the other. HV testing supplies the basic data (fingerprints) for diagnostics. In case of warnings by on-line diagnostic systems, off-line withstand and PD testing delivers the best possible information about defects and enables the classification of the risk. Because alternating voltage (AC) is the most important test voltage, the AC generation on site is considered. Frequency tuned resonant (ACRF) test systems are best adapted to on-site conditions. They can be simply combined with PD measuring equipment. The available ACRF test systems and their application to electric power equipment -from cable systems to power transformers - is introduced.

• Annual Conference of KIPS
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• 2003.05b
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• pp.1313-1316
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• 2003

핸드폰 진단시스템의 경우에는 기술이 Qualcom 사(社)에 의존적이기에 Qualcom사(社)에서 제공되어지는 DM 을 국내 업체들이 기술이전을 통해서 자사 핸드폰에 적당하게 수정되어 사용하고 있는 실정이다. 본 논문에서는 이러한 Qualcom DM spec.의 미공개와 기술부진 때문에 초래한 문제의 해결점으로 spec.의 개발단계부터 구현까지 하여 단말기의 소프트웨어 개발을 위한 테스트 장비로 사용될 수 있으며 단말기의 고장과 진단을 빠르게 쉽게 할 수 잇다. 또한 향후 프로토콜의 보완, 수정, 발전에 능동적으로 대처할 수 있게 하였다.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.150-153
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• 2009

본 논문은 설비 진단 모니터링을 구축함에 있어 RFID 기반 관리 시스템에서 RFID 주파수 특성을 분석하였다. 분석된 논문에서는 설비 고장진단을 위한 모니터링 서비스를 RFID 기반으로 지원하는데, 이 과정에서 위치관리 개념을 도입하기 위해 적용된 RFID 주파수 특성을 분석하였다.

• Journal of the Korean Society of Safety
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• v.31 no.3
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• pp.28-33
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• 2016

Since defects in the wheels of railway vehicles, which occur due to wears with the rail, cause serious damage to the running device, the diagnostic monitoring system for condition-based maintenance is required to secure the driving safety. In this paper, we studied to apply a useful Cepstrum analysis to detect periodic structure in spectrum among the vibration signal processing techniques for the fault diagnosis of a rotating body such as wheel. In order to analyze in variations of train velocity, the Cepstrum analysis was performed after a domain change of the vibration signal from time domain to rotation angle domain. When domains change, it is important to use a interpolation for a uniform interval of the rotation angle. Finally, the Cepstrum analysis for wheel flat detection was verified by using the vibration signal including the disturbance resulting from the rail irregularities and the vibration of bogie components.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.