• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.42-45
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• 2001

In the speed-sensorless induction motor control systems, only a few percents of error in current measurement badly deteriorates the control performance. And early detection and accomodation of the faults of current sensor is very important to enhance the reliability of the induction motor control system. In this paper, we propose two sensor fault detection schemes having desired functions; fault detection, isolation of failed sensor and compensation of fault effect. The two schemes operate in real-time and employ EKFs (Extended Kalman Filter) for residual generation. Simulation results show that the proposed schemes are very useful in maintaining the control performance of the induction motor driven servo systems even in the face of sensor faults.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.46-53
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine (SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel (KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• 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.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.86-92
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• 2009

As increasing of using induction motors, the induction motors faults cause serious damage to the industry. Therefore to find out faults of induction motor is recognized as important problem awaiting solution. But to make matters worse, the faults of induction motors often progress through long time. It means that early diagnosis is very important. Many researches have been progressed and general method of diagnosis is using vibration sensor to diagnose fault of induction motor. However, although it is reliability technique, it demands high price and it is difficult to use. This paper presents an implementation of technique for fault diagnosis of induction motor using wavelet transform based stator current and it is composed with algorithm that decides whether fault existence or not using C++ based on windows software. The algorithm will be accomplished in real-time using current data acquisition board and PC automatically with Neural Network algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1731-1739
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• 2000

The Vibration signals of a gear driving system is often associated with gear tooth faults. Many studies have been done on the detection of impulsive vibration signals, which characterize the breaka ge of a gear tooth. Also, most of the studies on gear fault diagnosis are only about the fault existence at one gear-pair. This study concerns on the several possible faults of a geared motor that has three gear pairs. The measurement and analysis on the vibration signals of a running geared motor shows the relationship between the gear faults and the vibration signals. This study also shows that adaptive interference canceling technique can be appropriately applicable to detect which gear-pair has the fault, and that wavelet is better than spectrogram to figure out the gear fault.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.37-44
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• 2014

This paper proposes a new diagnosis algorithm to detect broken rotor bars (BRBs) faults in induction motors. The proposed algorithm is composed of a frequency signal dimension order (FSDO) estimator and a fault decision module. The FSDO estimator finds a number of fault-related frequencies in the stator current signature. In the fault decision module, the fault diagnostic index from the FSDO estimator is used depending on the load conditions of the induction motors. Experimental results obtained in a 75 kW three-phase squirrel-cage induction motor show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to a zoom multiple signal classification (ZMUSIC) and a zoom estimation of signal parameters via rotational invariance techniques (ZESPRIT).

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.522-523
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• 2014

Analysis of faults in induction motors has become a major field of research due to importance of loss and damage reduction and maximum online performance of motors. There are several methods to analyze the faults in an induction motor from conventional Fourier transform to modern decision-making neural networks. Considering detectability of fault among all methods, a new fault detection solution has been proposed; it is called as frequency-domain Discrete Wavelet Transform (FD-DWT). In this method, the stator current is decomposed through series of low- and high-pass filters and consequently, the fault characteristics are more visible, because additional components have been reduced. The objective of this paper is early detection of input voltage unbalance in induction motor using wavelet transform in frequency domain. Experimental results show the effectiveness of the proposed method in early detection of faults.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.874-875
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• 2007

This paper introduces major potential faults of IPMSM and their simulation realization methods. The faults of IPMSM, generally, contain single-phase open circuit, single-phase or 3-phase short circuit, and uncontrolled generation. When different fault occurs, the circuit of total system including motor and inverter also will be changed. Therefore, it is necessary to analyze and establish independent model for each kind of fault. In this paper, first, the drive circuit is analyzed as different fault type. Then, the corresponding simulation results solved in Simulink@MATLAB are given. The absence of experiment results leads that the veracity of simulation results can not be verified, but the tendency will be explained by theory analysis.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.65-67
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• 1995

The squirrel cage rotors for induction motors may have several faults such as broken bars, bad spots in end ring, abnormal skew caused by improper processing. These faults have bad effect on the performance of the induction motor. This paper proposes the detecting technique of these faults by analyzing the current of the detecting electric magnet, using 2-D finite element method taking account of the rotor movement.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.3
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• pp.27-34
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• 2007

The rotor faults of induction motors may cause bad effects on the performance of the induction motor. This paper proposes the detecting technique of these faults by analyzing the waveform of the induced current and voltage of search coil using numerical analysis and the experiment. Several defective rotor bars are simulated to analyze the fault conditions-broken bars and high resistance of rotor bars. In order to prove the usefulness of the proposed method, we made an prototype experimental apparatus. The waveform of the induced voltages in search coil has the obvious characteristics and it is easy to differentiate the normal rotor from the abnormal one. The experimental results show that the proposed method is useful to detect the rotor fault conditions.