• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.464-466
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• 2001

The advantage of the squirrel cage induction motor is the brushless rotor. This advantage for operation and maintenance turns out to be a disadvantage for the detection of the cage rotor bar and endring defects, which means that the detection of cage faults is due to the measurement and analysis of only the stator input signals. The monitoring task in an inverter drive is complicated mainly because the voltage and current waveforms are nonsinusoidal and the high dv/dt values from fast switching inverterd distort the measurements. in this paper, we are going to discuss the detection method of broken rotor bar of squirrel cage induction motor by the motor current signal analysis(MCSA).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1589-1593
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• 2008

The condition of a high voltage motor was monitored with the motor performance monitor (MPM) at the motor control center. The MPM detected defects in the rotor bar and end ring and input power according to motor and load conditions. The assessment of the condition of a coal pulverizer motor indicated it was clearly in good condition in terms of the rotor bars, over voltage, and motor performance. However, the side bands at frequency, 56.48 Hz indicated existence of rotor end-ring fault. The large torque ripple indicated abnormal operating conditions. After visual inspection, it has been observed that an impeller blade of the circulating water pump was broken off causing the irregular torque pattern.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.873-878
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• 2002

Stator current spectrum by the fast Fourier transform (FFT) of current signals has been widely used for fault detection in induction motors. In this paper, we propose efficient rotor fault detection of Induction motors using stator current spectrum monitoring. The proposed method utilizes the mean absolute difference (MAD) between a Predetermined reference vector and a feature vector extracted from the stator current spectrum. Our proposed approach requires a smaller amount of computations when compared to fault detection algorithms based on neural networks, since it uses simple MAD criterion to detect rotor faults related broken rotor bars. Experimental results show that our proposed method can successively detect the rotor fault of the induction motor.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.862-865
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• 2004

Faults such as broken rotor bars, static and dynamic eccentricity are often reported in induction motors. These faults increase the down-time of equipment, which causes major loss of earnings to the industry. This paper presents a result of the finite-element(FE) analysis of air-gap flux variation in induction motors when rotor vibration conditions occur, An accurate modelling and analysis of rotor vibration in the machine are developed using FE software packages, and measuring the flux are made using the search coils. In the FE analysis, an induction motor with 380 [V], 5[HP], 4 Poles, 1742 [rpm] ratings is used. The results of FE analysis can be used for on-line vibration monitoring of the induction motors.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.3
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• pp.420-427
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• 2006

This paper describes design and characteristic analysis of the 200[kW], 3000[rpm] induction motor for gearless turbo machine. It was designed by the loading distribution method and the results of characteristics obtained by the equivalent circuit method are compared with the results of circle diagram. To verify the validation of design 2D finite element method is used and also 3D finite element method is used to calculate the current density curve of the rotor bars when they are broken.

• 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 Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.5
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• pp.23-30
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• 2014

This paper presents an on-line diagnosis system for identifying health and faulted conditions in squirrel-cage induction motors using stator current, temperature, and partial discharge signals. The proposed diagnosis system can diagnose induction motor faults such as broken rotor bars, air-gap eccentricities, stator winding insulations, and bearing faults. Experimental results obtained from induction motors show that the proposed system is capable of detecting induction motor faults.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.874-882
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• 2003

The condition assessment of engineering systems has increased in importance because the manpower needed to operate and supervise various plants has been reduced. Especially, induction motors are at the core of most engineering processes, and there is an indispensable need to monitor their health and performance. So detection and diagnosis of motor faults is a base to improve efficiency of the industrial plant. In this paper, a model-based fault detection system is developed for induction motors, using steady state vibration signals. Early various fault detection systems using vibration signals are a trivial method and those methods are prone to have missed fault or false alarms. The suggested motor fault detection system was developed using a model-based reference value. The stationary signal had been extracted from the non-stationary signal using a data segmentation method. The signal processing method applied in this research is FFT. A reference model with spectra signal is developed and then the residuals of the vibration signal are generated. The ratio of RMS values of vibration residuals is proposed as a fault indicator for detecting faults. The developed fault detection system is tested on 800 hp motor and it is shown to be effective for detecting faults in the air-gap eccentricities and broken rotor bars. The suggested system is shown to be effective for reducing missed faults and false alarms. Moreover, the suggested system has advantages in the automation of fault detection algorithms in a random signal system, and the reference model is not complicated.