• Journal of Advanced Marine Engineering and Technology
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• v.37 no.5
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• pp.520-526
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• 2013

As the increasing of capacity and technology of power facilities, rotating machines are getting higher at capacity and voltage scale. Thus the monitoring and diagnosis of generators for fault detection has attracted intensive interest. In this paper, we developed fault diagnosis system for monitoring the fault operations in bad power systems. In order to verify the performance of this fault diagnosis system, we made the small scaled testing system which has the same winding structure of the real synchronous generator. The magnetic flux patterns in air-gap of a small-scale generator under various fault states as well as a normal state are tested by hall sensors and the fault detection system.

• 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.

• 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.

• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.251-253
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• 2002

Various measures of power quality are addressed, specifically power factor, power variability, electrical transients, flicker and harmonics. This paper presents the measurement criteria for monitoring and diagnosis of power quality according to the distribution system interconnection of wind turbine.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.10a
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• pp.36-39
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• 2000

This paper presents the technology of condition diagnosis & life estimation on insulation system of the traction motor. In the non-destructive methods for diagnosis of coil insulation state, residual dielectric strength is estimated by the D-map which consist of the partial discharge quantity Q and average degradation degree $\Delta$. In the operating history of machine, the N-Y life estimation method is based on the stop-starting numbers and operating times with considering each degradation factor by the thermal, electrical and heat-cycle stress. With the on-line conditioning monitoring on the currents of traction motors, detecting the abnormal operating state due to bearing faults, stator or armature faults, eccentricity related faults and broken rotor bars can be performed.

• Journal of the Korean Society of Safety
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• v.19 no.1
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3383-3388
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• 2007

To compare the sensor performance of AE leak diagnosis system which can measure valve leak conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured on valve of the simulated test system for power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, pressure difference, valve size and fluid using both piezoelectric acoustic emission sensor and Pb-Free acoustic emission sensor. The results of this study are utilized to select the type of sensors, the frequency band for filtering and thereby to improve the signal-to-noise ratio for diagnosis or monitoring of valves in operation. As the final result of application study above, portable type leak diagnosis system by AE was developed. The outcome of the study can be definitely applied as a means of the diagnosis or monitoring system for energy saving and prevention of accident for power plant valve.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.350-354
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• 1995

The Tension Leveller of Cold Rolling Mill In POSCO performs levelling the strip in high speed line. But minor variations in operating condition of driving machines such as motor, gear box, and support bearings, a small gap-variation of supporter and strip slip by poor roll revolutions can cause serious problems in the quality of strip. In this study, firstly, A condition monitoring standard for each sensor is made through with the detail analysis of vibration and strip slip. Secondly, An automatic monitoring and diagnosing system was developed to monitor the condition of Tension Leveller, and diagnose the cause of abnormal condition. Finally, A diagnosing algorithm for abnormal condition and man-machine interface (MMI) for easy operation are developed.

• Asian-Australasian Journal of Animal Sciences
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• v.10 no.6
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• pp.603-608
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• 1997

To improve animal production, a simple and accurate pregnancy diagnosis plays a very important role. Therefore, the purpose of this study was to develop an on-farm blood progesterone enzyme immunoassay (EIA) system for monitoring the early pregnancy in sows. Star tubes coated with mouse monoclonal anti-progesterone antibody were used for this proposed EIA system which was tested in field trials. The results could be obtained within 30 minutes either by spectrophotometry or the naked eye. Heparinized fresh blood samples collected from the ear vein of sows 17-22 days after breeding (day 0) were tested qualitatively to diagnose sows as pregnant or non-pregnant with high ( > 3 ng/ml) or low ($${{\leq_-}}3ng/ml$$) progesterone in the blood. To provided a double check data, plasma progesterone levels were also measured quantitatively by the same EIA system with some modification. Total agreement of diagnosis by the on-farm EIA kit and by farrowing or abortion from 128 tested sows was found to be 92.2% accuracy (93.1% on pregnant diagnosis and 83.3% on non-pregnant diagnosis). It was concluded that the on-farm EIA blood progesterone test is a very useful method for monitoring the early pregnancy status of sows.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.27-30
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• 2019

Factory monitoring systems are gaining importance in wide areas of industry. Especially, there have been many efforts in implementation of vibration measurement and analysis for monitoring the status of rotating machines. In this paper, a digital signal processor (DSP) based monitoring system dedicated to the vibration monitoring and analysis on rotating machines is discussed. Vibration signals are acquired and processed for the continuous monitoring of the machine status. Time domain signals and fast Fourier transform (FFT) are used for vibration analysis. All of the signal processing procedures are done in the DSP to reduce the production and maintenance cost. The developed system could also provide remote and mobile monitoring capabilities to operator via internet connection. This paper describes the overview of the functional blocks of the implemented system. Test results based on signals from small-size single phase motors are discussed for monitoring and defect diagnosis of the machine status.