• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.8
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• pp.787-795
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• 2001

An experimentation on the on-line fault detection and diagnosis(FDD) system has been performed with HVAC system in he experimental building constructed inside the large scale environmental chamber. Personal computer with a home-made FDD program by pattern recognition method utilizing artificial neural network was connected on-line via Ether-net TCP/IP to the supervisory control server for HVAC system. The FDD program monitored the HVAC system by 1 minuted interval. The results showed that he FDD program detected the sudden or abrupt faults such s those in fans, sensors and heater, etc.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1906-1908
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• 2000

The central monitoring system with on-line diagnosis of high-voltage generator/motor stator insulation is developed. The system is capable of remote diagnosis and monitoring partial discharges of high-voltage generator/motor stator insulation. GOMS(Generator On-line Monitoring System) with maximum of 9 input channels can measure and analyze the status of high-voltage motor stator insulation by on-line. The measured and analysis data are brought to the central monitoring system via modem to build database. The central monitoring system can diagnose and monitor the insulation status of several high-voltage generator/motor at any time. The insulation status of those machines can be enhanced by the database on partial discharges.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.05b
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• pp.43-46
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• 2004

Recently many research activities on the diagnosis of stator winding insulation of high-voltage motors have been reported. Most of them utilize a trend analysis of partial discharges. In this paper, a novel on-line monitoring system for an insulation diagnosis is proposed. This system displays magnitude and phase angle distribution of partial discharge. The partial discharge activities can be interpreted more precisely by these techniques.

• Journal of the Korean Institute of Gas
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• v.4 no.4 s.12
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• pp.42-49
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• 2000

Due to their size and complexity, it is very difficult to make diagnostic system for the whole chemical processes. Therefore, a systematic approach is required to decompose larpge-scale process into sub-processes and then diagnose them. This paper suggests a method for the minimization of knowledge base and flexible diagnosis to be used in qualitative fault diagnosis based on Fault-Effect Tree model. The system can be decomposed for flexible diagnosis, size reduction of knowledge base, and consistent construction of complex knowledge base. The new node, gate-variable, is introduced to connect the cause-effect relationships of each sub-process. For on-line diagnosis, off-line analysis is performed to construct Fault-Effect Trees of gate-variables as well as activation conditions of gate-variables. On-line diagnosis strategy is modified to get the same diagnosis result without system decomposition. The proposed method is illustrated with a fault diagnosis system for a large-scale boiler plant.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.154-156
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• 1993

In this paper, fault analysis using simulation method and fault diagnosis scheme are presented for induction motor drive system. Major faults such as inverter 'a' phase open fault, inverter 'a'-'b' phase short circuit fault and inverter 'a' phase ground fault are analyzed and simulated. On-line and off-line fault diagnosis systems are proposed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.11
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• pp.73-82
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• 2011

The solid insulation system of stator windings in high voltage rotational machines is usually aging in accordance with long time operation. The partial discharge test have been known to check whether insulation deterioration exist or not, and the PD test can effectively diagnose a solid insulation condition regardless of during operation(on-line) or not(off-line). The on-line PD measurement have proven to be a successful technique in monitoring stator insulation condition nowadays. This paper describes the characteristics of comparing the on-line PD measurement data using PDMS-HG(Partial Discharge Monitoring System for Hydro-electric Generator) installed in field with the off-line diagnosis measurement data(insulation resistance, winding resistance, PI, ${\Delta}tan{\delta}$ and PD) on hydro generator(13[kV]) stator windings. These results make good use of managing rotational machines through evaluating the solid insulation condition of stator windings.

• Proceedings of the KIEE Conference
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• 1998.07e
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• pp.1734-1736
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• 1998

On-line partial discharge monitoring system for generator stator insulation is developed. This system consists of remote and host units. The remote unit detects partial discharge signals from SSC(Stator Slot Coupler) installed between wedge and stator windings. The host unit monitors the condition of winding insulation. This system will be used as a module of a generator on-line monitoring system utilizing global network.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.4
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• pp.206-210
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• 2004

This paper deals with a parameter estimation of the DC-DC converter system for its diagnosis. Especially, we present the results of parameter estimation for the DC-DC converter model by the system identification method. The parameter estimation for the DC-DC converter system aims at the diagnosis of its operating status. For the operating status diagnosis of the DC-DC converter system, we assume that the DC-DC converter system is an equivalent model of the Buck converter and estimate the main parameter for on-line diagnosis. In addition, for verification of an estimated parameter, we compare a bode plot of the estimated system transfer function and measurement results of the HP4194 instrument. It is a control system analyzer for system transfer function measurement. Our results confirm that the main parameter for diagnosis of the DC-DC converter system can be estimated by the system identification method and that the aging status of the system can be predicted by these results on operating status.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.04a
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• pp.321-327
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• 1998

This paper presented a case study on the application of on-line wear monitoring technique to a high duty air-turbo-compressor system. Main objects monitored were a gear unit and metal bearings, both shown frequent troubles due to the severe operation conditions at heavy dynamic load. The air-turbo-compressor system needs secure condition monitoring because it is one of the main utilities in steel making industry. Temperature and vibration characteristics have been mainly on-line monitored in this system for a predictive maintenance; however, it has been shown that they are not fairly good enough to give an early warning prior to the machine failure. In this work, an on-line Opto Magnetic Detector(OMD) was implemented for an on-line wear monitoring, which quantitatively measured the contamination level of both ferrous and non-ferrous wear particles by detecting the change in optical density of used oil. Results showed that the application of on-line OMD system was satisfactory in diagnosis of the machine system.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.233-238
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• 1993

In this paper, two diagnosis algorithms using the simple fuzzy, cognitive map (FCM) that is an useful qualitative model are proposed. The first basic algorithm is considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. And the second one is an extended version of the basic algorithm. In the extension, three important concepts, modified temporal associative memory (TAM) recall, temporal pattern matching algorithm and hierarchical decomposition are adopted. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large scale and complex processes that conventional diagnosis methods cannot be applied. The diagnosis system can be trained by the basic algorithm and generates FCM model for every experienced process fault. In on-line application, the self-generated fault model FCM generates predicted pattern sequences, which are compared with observed pattern sequences to declare the origin of fault. In practical case, observed pattern sequences depend on transport time. So if predicted pattern sequences are different from observed ones, the time weighted FCM with transport delay can be used to generate predicted ones. The fault diagnosis procedure can be completed during the actual propagation since pattern sequences of tvo different faults do not coincide in general.