• 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.

• 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.

• Plant Journal
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• v.11 no.2
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• pp.39-44
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• 2015

According to a research that is based on a previous study, But in a different way, This study shows fault diagnosis of Induction motor bearing which runs in coal-fired power plant industries on Desulfurization absorber agitator using Spectrum analysis of Stator Current and visual inspection. As a result of harmonic content analysis of stator current spectrum, It was possible to detect ball and outer race fault frequency. The comparison in the context of this experiment proves that the amplitude of faulty frequency is increased in three times at a fault in ball and in outer race. Spectrum analysis of stator current can be used to detect the presence of a fault condition as well as experiment in faulty bearings, besides early fault detection in bearings can prevent unexpected power generation loss and emergency maintenance cost.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1439-1444
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• 2005

Induction motors are the most commonly used electrical drives because they are rugged, mechanically simple, adaptable to widely different operating conditions, and simple to control. The most common faults in squirrel-cage induction motors are bearing, stator and rotor faults. Surveys conducted by the IEEE and EPRI show that the most common fault in induction motor is bearing failure (${\sim}$40% of failure). Thence, this paper addresses experimental results for diagnosing faults with different rolling element bearing damage via motor current spectral analysis. Rolling element bearings generally consist of two rings, an inner and outer, between which a set of balls or rollers rotate in raceways. We set the experimental test bed to detect the rolling-element bearing misalignment of 3 type induction motors with normal condition bearing system, shaft deflection system by external force and a hole drilled through the outer race of the shaft end bearing of the four pole test motor. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by incipient bearing failure. The failure modes are reviewed and the characteristics of bearing frequency associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and related frequencies are also determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. We utilized the FFT, Wavelet analysis and averaging signal pattern by inner product tool to analyze stator current components. The test results clearly illustrate that the stator signature can be used to identify the presence of a bearing fault.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.2
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• pp.294-300
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• 1996

In hydro-generator, a groundwall insulation of stator windings gradually deteriorates due to mechanical, thermal, electrical and environmental stresses. These stresses combine to result in loose windings, delamination of the stator insulation and/or electrical tracking of the endwinding, all of which can lead to stator insulation failures. Conventionally, off-line tests such as partial discharge measurement, DC/AC current and .DELTA.tan.delta. tests has been used for estimation of winding condition. However, off-line test requires large power supply and generator outage. In addition, major cause of insulation problems such as loose wedges and slot dischages may not be found with off-line diagnoses. This paper introduces the on-line partial discharge measurement techniques using frequency spectrum analyzer(FSA) for the generator stator windings. The experimental results from the UIAM #1 hydro-generator confirms a optimistic application of on-line generator diagnosis method as a reliable tool for evaluation of winding condition.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.635-640
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• 2014

Detection and diagnosis of incipient bearing fault in an induction motor is important for the prevention of serious motor failure. This paper presents an analysis of the effect of a faulty bearing on the stator current of an induction motor. A bearing fault leads to torque oscillations which result in phase modulation of the stator current. Since the torque oscillations cause specific frequency components at the stator current spectrum to rise sharply, the bearing fault can be detected by checking out the faultrelated frequency. In this paper, a mathematical model of the load torque oscillation caused by a bearing fault is presented. The proposed model can be used to analyze the physical phenomenon of a bearing fault in an induction motor. In order to represent the bearing fault effect, the proposed model is combined with an existing model of vector-controlled induction motors. A set of simulation results demonstrate the effectiveness of the proposed model and represent that bearing fault detection using a stator current is useful for vector-controlled induction motors.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.6
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• pp.36-45
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• 2005

This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. We set the experimental test bed. They is composed of the normal condition bearing system, the abnormal rolling-element bearing system of 2 type induction motors with shaft deflection system by external force and a hole drilled through the outer race of the shaft end bearing of the four pole test motor. We have developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current The effects on the stator current spectrum are described and related frequencies are also determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. We utilized the FFT(Fast Fourier Transform), Wavelet analysis and averaging signal pattern by inner product tool to analyze stator current components. Especially, the analyzed results by inner product clearly illustrate that the stator signature analysis can be used to identify the presence of a bearing fault.

• Journal of Power Electronics
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• v.7 no.4
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• pp.271-277
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• 2007

In this paper, an induction motor rotor fault diagnosis system using current signals, which are measured using the axis-transformation method is presented. Inverter-fed motor drives, unlike line-driven motor drives, have stator currents which are rich in harmonics and therefore fault diagnosis using stator current is not trivial. The current signals for rotor fault diagnosis need precise and high resolution information, which means the diagnosis system demands additional hardware such as a low pass filter, high resolution ADC, an encoder and additional hardware. Therefore, the proposed axis-transformation method is expected to contribute to a low cost fault diagnosis system in inverter-fed motor drives without the need for any additional hardware. In order to confirm the validity of the developed algorithms, various experiments for rotor faults are tested and the line current spectrum of each faulty situation, using the Park transformation, is compared with the results obtained from the FFT(Fast Fourier Transform).

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.500-504
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• 2007

In this paper, the induction motor rotor fault diagnosis system using current signals, which are measured using axis-transformation method, and speed, which is estimated using current information, are presented. In inverter-fed motor drives unlike line-driven motor drives the stator currents have numerous harmonics components and therefore fault diagnosis using stator currents is very difficult. The current and speed signal for rotor fault diagnosis needs to be precise. Also, high resolution information, which means the diagnosis system, demands additional hardware such as low pass filter, high resolution ADC, encoder and etc. Therefore, the proposed axis-transformation and speed estimation method are expected to contribute to low cost fault diagnosis systems in inverter-fed motor drives without the need for an encoder and any additional hardware. In order to confirm validity of the developed algorithms, various experiments for rotor faults are tested and the line current spectrum of each faulty situation using Park transformation and speed estimation method are compared with the results obtained from fast Fourier transforms.

• Journal of Power Electronics
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• v.9 no.2
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• pp.224-231
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• 2009

The induction motor rotor fault diagnosis system using current signals, which are measured using an axis-transformation method, is presented in this paper. In inverter-fed motor drives, unlike line-driven motor drives, the stator currents are rich in harmonics; therefore fault diagnosis using stator current is not trivial. The current signals for rotor fault diagnosis need precise and high resolution information, which means the diagnosis system demands additional hardware such as a low pass filter, high resolution ADC, and encoder, etc. The proposed axis-transformation method with encoder and without encoder is expected to contribute to a low cost fault diagnosis system in inverter-fed motor drives without the need for any additional hardware. In order to confirm the validity of the developed algorithms, various experiments for rotor faults are tested and the line current spectrum of each faulty situation using Park transformation is compared with the results obtained from fast Fourier transforms.