• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.15 no.1
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• pp.93-101
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• 2002

In this study, the stator form-winding sample coils based on silicone resin and polyimide were made for fault prediction and reliability estimation on the C-Class(200$\^{C}$ ) insulation system of traction motors. The complex accelerative degradation was periodically performed during 10 cycles, which was composed of thermal stress, fast rising surge voltage, vibration, water immersion and overvoltage applying. After aging of 10 cycles, the condition diagnosis test such as insulation resistance '||'&'||' polarization index, capacitance '||'&'||' dielectric loss and partial discharge properties were investigated in the temperature range of 20 ∼ 160$\^{C}$. Relationship among condition diagnosis tests was analyzed to find a dominative degradation factor and an insulation state at end-life point.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.763-766
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• 2000

In this paper, stator form-winding sample coils based on silicone resin and polyimide were made for fault prediction and reliability estimation on the 200 Class insulation system of traction motors. The complex accelerative degradation was performed by periods during 10 cycles, which was composed of thermal stress, fast rising surge voltage, vibration, water immersion and overvoltage applying. After aging of 10 cycles, condition diagnosis test such as insulation resistance & polarization index, capacitance & dielectric loss and partial discharge properties were investigated in the temperature range of 20∼160$^{\circ}C$. Relationship among condition diagnosis test was analyzed to find an dominative degradation factor and an insulation state at end-life point.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.835-841
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• 1997

Microscopic examination of the morphology of wear debris is an accepted method for machine condition and fault diagnosis. However wear particle analysis has not been widely accepted in industry because it is dependent on expert interpretation of particle morphology and subjective assessment criteria. This paper was undertaken to analyze the morphology of wear debris for machine condition diagnosis of the lubricated moving surfaces by image processing and analysis. The lubricating wear test was performed under different sliding conditions using a wear test device made in our laboratory and wear testing specimen of the pin-on-disk-type was rubbed in paraffine series base oil. In order to describe characteristics of debris of various shape and size, four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) have been developed and outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology in machine condition monitoring, thus to overcome many of the difficulties in current methods and to facilitate wider use of wear particle analysis in machine condition monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.1
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• pp.23-29
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• 2016

In this paper, a real-time condition diagnosis system for the lens injection molding process is developed through the use of LabVIEW. The built-in-sensor (BIS) mold, which has pressure and temperature sensors in their cavities, is used to capture real-time signals. The measured pressure and temperature signals are processed to obtain features such as maximum cavity pressure, holding pressure and maximum temperature by the feature extraction algorithm. Using those features, an injection molding condition diagnosis model is established based on a response surface methodology (RSM). In the real-time system using LabVIEW, the front panels of the data loading and setting, feature extraction and condition diagnosis are realized. The developed system is applied in a real industrial site, and a series of injection molding experiments are conducted. Experimental results show that the average real-time condition diagnosis rate is 96%, and applicability and validity of the developed real-time system are verified.

• Journal of Drive and Control
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• v.18 no.1
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• Journal of Applied Reliability
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• v.14 no.4
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• pp.208-212
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• 2014

An experimental gear-box was set-up to simulate the real situation of the wind-turbine. Artificial cracks of different sizes were machined into the gear. Vibration signals were acquired to diagnose the different crack fault conditions. Time-domain features such as root mean square, variance, kurtosis, normalized 6th central moments were used to capture the characteristics of different crack conditions. Normal condition, 1 mm crack condition, 2mm crack condition, 6mm crack condition, and tooth fault condition were compared using ANFIS and DAG-SVM methods, and three different DAG-SVM models were compared. High-pass filtering improved the success rates remarkably in the case of DAG-SVM.

• Tribology and Lubricants
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• v.14 no.3
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• pp.74-80
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• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.

• Journal of Power System Engineering
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• v.17 no.4
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• pp.31-35
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• 2013

The emergency AC power supply system of the nuclear power plant is designed to supply the power to the nuclear power plant at the emergency operating condition. The safety function of the diesel generator at the nuclear power plant is to supply AC electric power to the safety system whenever the preferred AC power supply is unavailable. The reliable operation of onsite standby diesel generator should be ensured by a condition monitoring system designed to maintain, monitor and forecast the reliability level of diesel generator. The purpose of this paper is to improve the existing ultrasonic sensor used for condition diagnosis of engine fuel pump and cylinder head for the accurate diagnosis in actual engine condition of emergency diesel generator(EDG). As a result of this study, we could design and develop much more reliable ultrasonic sensor than existing ones.

• International Journal of Automotive Technology
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• v.6 no.4
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• pp.341-350
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• 2005

In this study, fault diagnosis and monitoring of serial wound pre-engaged starter motors have been carried out. Starter motors are DC motors that enable internal combustion engine (ICE) to run. In case of breakdown of a starter motor, internal combustion engine can not be worked. Starter motors have vital importance on internal combustion engines. Kohonen network based fault diagnosis system is proposed for fault diagnosis and monitoring of starter motors. A graphical user interface (GUI) software has been developed by using Visual Basic 6.0 for fault diagnosis. Six faults, seen in starter motors, have been diagnosed successfully by using the developed fault diagnosis system. GUI software makes it possible to diagnose the faults in starter motors before they occur by keeping fault records of past occurrences.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.363-369
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• 2019

Various studies have been conducted for the diagnosis of noise condition of complex rotary machines. In this study, diagnosis index using vibration signal is developed for the efficient and objective assessment of noise condition of a blower motor. The noise most commonly caused by the abnormal blower motor are Tick/Click noise and Tone noise. According to cause and noise characteristics, time-frequency analysis is used to diagnose Tick/Click noise, and smoothing in frequency domain is used to diagnose tone noise condition. The noise condition of the blower motors were diagnosed using the developed index and these results are compared with the diagnostic results by the experts. As a result, the agreement rate was about 95 %.