• Proceedings of the KIEE Conference
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• 2002.07c
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• pp.1701-1703
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• 2002

This paper introduces a on-line monitoring system for insulation diagnosis of high-voltage motor stator windings. This system monitors the insulation condition of the stator winding with sensor, coupling capacitor. Partial discharge (PD) signals are able to be continuously measured and digitalized with a Peak-hold A/D converter to build the database of the high-voltage motor's insulation condition. This system can communicate with the central monitoring system via RS-485 This paper introduces an economic solution for data acquisition of partial discharges.

• Proceedings of the KIEE Conference
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• 1999.11d
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• pp.1025-1027
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• 1999

Recently, many researches on a diagnosis of stator winding insulation of large generators are reported. They mostly utilize a trend analysis of Partial Discharge (PD). In this paper, a novel on-line monitoring system for an insulation diagnosis is proposed. This system obtains the parameters such as Maximum Partial Discharge Magnitude (QM), Normalized Quantity Number (NQN) and Dynamic Stagnation Voltage (DSV) by continuous on-line monitoring of winding insulation. It is capable of diagnosing the insulation condition by analyzing the trend of PD and utilizing the database built by the system.

• Tribology and Lubricants
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• v.18 no.2
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• pp.117-126
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• 2002

Abstract - An integrated condition monitoring algorithm based on an expert system was implemented in this work in order to monitor effectively the machine conditions. The knowledge base was consisted of numeric data which meant the posterior probability of each measurement parameter for the representative machine failures. Also the inference engine was constructed as a series of statistical process, where the probable machine fault was inferred by a mapping technology of pattern recognition. The proposed algorithm was, through the user interface, applied for an air compressor system where the temperature, vibration and wear properties were measured simultaneously. The result of the case study was found fairly satisfactory in the diagnosis of the machine condition since the predicted result was well correlated to the machine fault occurred.

• Structural Engineering and Mechanics
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• v.88 no.6
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• pp.569-581
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• 2023

This paper introduces a system to detect and diagnose anomalies in pumped storage hydropower plants. We collect data from various types of sensors, including those monitoring temperature, vibration, and power. The data are classified according to the operation modes (pump and turbine operation modes) and normalized to remove the influence of the external environment. To detect anomalies and diagnose their types, we adopt a multivariate normal distribution analysis by learning the distribution of the normal data. The feasibility of the proposed system is evaluated using actual monitoring data of a pumped storage hydropower plant. The proposed system can be used to implement condition monitoring systems for other plants through modifications.

• Journal of the Korean Institute of Gas
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• v.2 no.3
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• pp.54-59
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• 1998

As distributed control systems (DCS) and plant information systems (PIS) are introduced into gas industries, process monitoring systems based on process data have attracted significant interests. However, these technologies have not been fully due to strong nonlinearities of batch processes. The multiway principal component analysis, which has been recently developed, has solved these problems and has been widely used in the industries. However, the lack of statistical background of process operators has been one of major obstacles for maximum utilization of the technology This paper introduces a real time monitoring system for batch gas manufacturing processes that offers a variety of tools that operators can understand and use without serious difficulties. The proposed integrated system covers the whole spectrum of monitoring and diagnosis that include data collection, monitoring and diagnosis. The developed system has been verified to be very effective for monitoring and diagnosis using its application to the construction of monitoring system for a typical industrial batch reactor.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.253-259
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• 2006

There is few study which automatically diagnoses the fault from ship's monitored data. The bigger control and monitoring system is. the more important fault diagnosis and maintenance is to reduce damage caused by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault from monitored data and is composed of fault detection knowledge base and fault diagnosis knowledge base. For all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem, To verify capability of fault detection, diagnosis and prediction, FMS(Fault Management System) is developed by C++. Simulation by FMS is carried out with population data set made by the log book data of 2 months duration from a large full container ship of H shipping company.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.251-256
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• 2005

There is few study which automatically diagnose the fault from ship's monitored signal. The bigger control and monitoring system is, the more important fault diagnosis and maintenance is to reduce damage brought forth by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault and is composed to fault detection knowledge base and fault diagnosis knowledge base. For this all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem by analyzing ship's operation data. To verifying capability of fault detection, diagnosis and prediction, Fault Management System(FMS) is developed by C++. Simulation experiment by FMS is carried out with population data set made by log book data of 2 months duration from a large full container ship of H shipping company.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.1
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• pp.56-62
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• 2007

In this paper, design method for power tower hazard diagnosis/predition system based on UsN was proposed. The proposed method used multi-hybrid sensors to measure rotation, displacement, and inclination state of power tower, and made decision/prediction of hazard of power tower. System design was made with requirement analysis of monitoring for transmission power facility and use of MEMS and optic fiber sensors. For hazard decision, analysis of correlation was made using sensor output. LN based on IEC61850,international standard for digital substation, was also proposed. For transmission facility monitoring, digital substation and power tower were considered as parts of power facility networks.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• Proceedings of the KIEE Conference
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• summer
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• pp.622-624
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• 2004

To reduce the effect of fault on power systems, like GIS and transformers, power system authorities are using various technologies to monitor and diagnose there facilities. Developing On-Line monitoring systems by using IT technology is main issue of nowadays for power system authorities. Among various monitoring and diagnosis technologies, gas sensing technologies can be most useful candidate because large power systems are using gas and oils for there insulation and analyzing density of gases that are included in the gas and oils for insulation purpose tell us what kind of reaction were arisen. In this paper, we describe on the gas sensing technology that are used for power systems monitoring and diagnosis.