• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.11-19
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• 2013

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.635-637
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• 2001

The faults of the squirrel cage induction motor is grew increasingly complex as the faults resulting in the shorting of a stator winding and the broken rotor bar or cracked rotor end ring, bearing faults, and so on. The users of electrical machines initially relied on simple protections such as over-current, over-voltage, earth-fault, etc. to ensure safe and reliable operation. but this method cause heavy financial losses and the threat of safety therefore it has now become very important to diagnose faults at there very inception. 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) and the opening terminal voltage signal analysis.

• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.644-647
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• 2003

The predictive maintenance can help to avoid the serious plant breakdowns and catastrophies. This paper deals with the fault diagnosis of the rotor of the induction motor which is widely used in the plants. In order to detect the broken bar, the Extended Kalman Filter is adopted to estimate the rotor resistance on the base of model-based method. The proposed estimation method is simulated with the aid of Matlab.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1955-1962
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• 2017

Induction motors are a workhorse for the industry. The condition monitoring and fault analysis are the main concern for the engineers. The bearing is one of the vital segment of the induction machine and the condition of the whole machine is decided based on the condition of the bearing. In the present paper, the vibration signal of the bearing has been used for the analysis. The first line of action is to perform a statistical analysis of the vibration signal which gives trends in signal. To get the location of a fault in the bearing the second action is to develop an index based on Wavelet Packet Transform node energy named as Bearing Damage Index (BDI). Further, Teager-Kaiser Energy Operator (TKEO) has been calculated from higher index value to get the envelope and finally Power Spectral Density (PSD) has been applied to identify the fault frequencies. A performance index has also been developed to compare the usefulness of the proposed method with other existing methods. The result shows that the strong amplitude of fault characteristics and its side bands help to decide the type of fault present in the recorded signal obtained from the bearing.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.1
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• pp.51-55
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• 2007

In this paper, a portable rotor bar fault diagnosis system for small 3 phase induction motors is suggested. For portable real-tine diagnosis system, an USB-DAQ board for collecting the 3 phase current data, three current probes, and a notebook computer are used. The LabVIEW graphical language is used for filtering, analysis, storing, and monitoring the current data. The three phase stator current are filtered and transformed to frequency level by FIT. An analysis window programed by LabVIEW is located in front panel to show the FIT results and this suggested window has a zooming function to detect the fault feature more easily near the feature frequency range which is varying by the slip frequency. To show the possibility of portable rotor bar diagnosis system, three types(healthy, one rotor bar fault, two rotor bar fault) of rotor bar are intentionally prepared and compared by the suggested window of front panel. Experimental results are shown that a suggested diagnosis system is applicable to portable diagnosis system and the rotor bar fault is detected by the frequency window in front panel programed in LabVIEW graphical language.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.549-555
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• 2003

The technology of machine condition monitoring is used effectively to detect the machine faults at an early stage using different machine quantities, such as current, voltage, temperature and vibration. Induction motors are most widely used to drive pumps, compressors and fans in industrial drives. This paper presents approach to data fusion using Dempster-Shafer theory because only one technique has uncertainty. So we can obtain advanced accuracy of the machine fault diagnosis. Vibration and current quantities are applied to diagnose three-phase induction motor.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.238-245
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• 2016

Inverters are considered the basic building blocks of industrial electrical drive systems that are widely used for various applications; however, the failure of electronic switches mainly affects the constancy of these inverters. For safe and reliable operation of an electrical drive system, faults in power electronic switches must be detected by an efficient system that is capable of identifying the type of faults. In this paper, an open switch fault identification technique for a three-phase inverter is presented. Single, double, and triple switching faults can be diagnosed using this method. The detection mechanism is based on stator current analysis. Discrete wavelet transform (DWT) using Daubechies is performed on the Clarke transformed (-) stator current and features are extracted from the wavelets. An artificial neural network is then used for the detection and identification of faults. To prove the feasibility of this method, a Simulink model of the DWT-based feature extraction scheme using a neural network for the proposed fault detection system in a three-phase inverter with an induction motor is briefly discussed with simulation results. The simulation results show that the designed system can detect faults quite efficiently, with the ability to differentiate between single and multiple switching faults.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.187-196
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• 2011

Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.802-808
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• 2000

This paper characterizes the electromechanical parameters due to the fault of rotor bars in a squirrel cage induction motor. Simulation is performed to investigate how broken rotor bars have effect on them by solving the time-stepping finite element equation coupled with magnetic field equation, circuit equation and mechanical equation of motion. It shows that the asymmetry of magnetic flux due to the broken rotor bar introduces the beating phenomenon in time domain and the sideband frequencies in frequency spectra, respectively, to the stator current, torque, speed, magnetic force and vibration of a rotor. However, vibration of a rotor would be the most effective monitoring parameters to detect the faults of rotor bars.