• Proceedings of the KSR Conference
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• 2005.11a
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• pp.44-48
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• 2005

In general, the parallel-connected linear induction motors(LIM) are fed by one VVVF inverter in the magnetically levitated vehicle(MAGLEV) or linear motor subway drives. The air gap length of the parallel-connected linear induction motors operating at a grade or curved sections can be different each other. The air gap difference of the two motors attached to the same module causes unequal phase currents, asymmetic thrust and attraction force generation. In this paper, parellel-connected linear induction motors are operated by one IGBT inverter under the different air gap condition so that the phase current characteristics are examined experimentally.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1882-1888
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• 2010

It is widely known that ESA(Electric Signature Analysis) method is very useful one for fault diagnosis of an induction motor. Online fault diagnosis system of induction motors using LabVIEW is proposed to detect the fault of broken rotor bars and shorted turns in stator. This system is not model-based system of induction motor but LabVIEW-based fault diagnosis system using FFT spectrum of stator current in faulty motor without estimating of motor parameters. FFT of stator current in faulty induction motor is measured and compared with various reference fault data in data base to diagnose the fault. This paper is focused on to predict and diagnose of the health state of induction motors in steady state. Also, it can be given to motor operator and maintenance team in order to enhance an availability and maintainability of induction motors. Experimental results are demonstrated that the proposed system is very useful to diagnose the fault and to implement the predictive maintenance of induction motors.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.3
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• pp.61-68
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• 2015

When medium and large induction motors are driven by 2-level inverters with low switching frequency, induction motors provoke deteriorated performances resulted from large torque ripples, flux ripples, and large current distortion. Model predictive torque control(MPTC) for a fast torque control of induction motors is also suffered from large torque ripples when the induction motors are fed by 2-level inverters that are based on 6 active voltage vectors with low switching frequency restricted. To solve this problem, this paper proposes a new MPTC method based on both a 12 active voltage vector and an optimized duty ratio calculation. The proposed control strategy illustrates its effectiveness under the various operating conditions through simulation works.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.702-705
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• 1996

Recently, induction motors are used more widely because of their low cost and simple structure. Therefore, the importance of fault detection and isolation of induction motors significantly increases. In most case the line current is used for fault detection and isolation. But in case that an induction motor has an inverter for control, it distorts the information of faulty state included in the line current. This paper proposes a new method for fault detection and isolation of induction motors that is speed controlled by the inverter using frequency analysis of the reference current instead of the line current for fault detection and isolation.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.2
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• pp.141-147
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• 2015

This paper deals with thermal characteristic analysis of induction motors for machine tool spindle for motion error prediction. Firstly, the inverse design of general induction motors for machine tool spindle has been performed by design principles. Characteristics considering VVVF inverter of induction motors were analyzed. Secondary, power loss and thermal characteristics of induction motors analyzed by equivalent thermal resistance model from Motor-CAD S/W. To develop a second-order fitted power-loss distribution model for the constant-torque operating range of the induction motor, we employed the design of experiment and response surface methodology techniques. Finally, the analysis results were experimentally verified, and the validity of the proposed analysis method was confirmed.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2222-2224
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• 2002

Induction motors are critical components of many industrial machines and are frequently integrated in commercial equipment. The heavy economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motors supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the artificial neural network, and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1892-1896
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• 2008

Inverter-fed induction motors (IFM) are prevalent in traction vehicles. However, the winding insulation of IFM is substantially more stressed than of line-powered motors by surge voltages. Consequently, the winding insulation of IFM should be estimated by surge voltages. Also, the weakness of coil insulation can be detected by the surge voltage test. This paper described the insulation evaluation of induction motors by application of surge voltages. A surge voltage generator with the maximum voltage of 5 kV and the selectable rise-time in ranges of $50\;ns\;{\sim}\;500\;ns$ was fabricated. In the experiment, we applied surge voltages into induction motors with the magnitude and the risetime according to IEEE 522. By the analysis of applied surge voltage and current waveforms, we could find difference between normal and defection windings.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.819-825
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• 2007

In this paper, we propose a fault diagnosis algorithm of induction motors by DFT and wavelet. We extract a feature vector using a fault pattern extraction method by DFT in frequency domain and wavelet transform in time-frequency domain. And then we deal with a fusion algorithm for the feature vectors extracted from DFT and wavelet to classify the faults of induction motors. Finally, we provide an experimental results that the proposed algorithm can be successfully applied to classify the several fault signals acquired from induction motors.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1079-1080
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• 2011

This paper deals with the operating characteristics of induction motors with broken rotor bar, stator winding inter-turn short and their complex fault conditions. The considered operating characteristics are phase current, torque and speed. Since the operating characteristics of induction motors are directly related to their slip conditions, this paper built the experimental set to adjust the speed of induction motor with a permanent magnet synchronous generator connected to a load bank. From the various experimental results, it is shown that the faults do not highly affect on the operating characteristics of induction motors in low slip conditions, but the fault characteristics can be easily found in larger slip conditions.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.822-827
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• 2004

In this paper, an intelligent fault diagnosis system is proposed for induction motors through the combination of feature extraction, genetic algorithm (GA) and neural network (ANN) techniques. Features are extracted from motor vibration signals, while reducing data transfers and making on-line application available. GA is used to select most significant features from whole feature database and optimize the ANN structure parameter. Optimized ANN diagnoses the condition of induction motors online after trained by the selected features. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origin on the induction motors. The results of the test indicate that the proposed system is promising for real time application.