• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.285-285
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• 2000

A continuous and reliable electrical energy supply is the objective of any power system operation. A transmission line is the part of the power system where faults are most likely to happen. This paper describes the use of wavelet transform for analyzing power system fault transients in order to determine the fault location. Synchronized sampling was made possible by precise time receivers based on GPS time reference, and the sampled data were analyzed using wavelet transform. This paper describes a fault location monitoring system and fault locating algorithm with GPS, DSP processor, and data acquisition board, and presents some experimental results and error analysis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.5
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• pp.242-250
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• 2005

This paper proposes a new protective relaying algorithm using Neuro-Fuzzy and wavelet transform. To organize advanced nuero-fuzzy algorithm, it is important to select target data reflecting various transformer transient states. These data are made of changing-rates of Dl coefficient and RSM value within half cycle after fault occurrence. Subsequently, advanced neuro-fuzzy algorithm is obtained by converging the target data. As a result of applying the advanced neuro-fuzzy algorithm, discrimination between internal fault and inrush is correctly distinguished within 1/2 after fault occurrence. Accordingly, it is evaluated that the proposed algorithm can effectively protect a transformer by correcting discrimination between winding fault and inrushing state.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.640-641
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• 2007

In this paper, the discrete wavelet transform (DWT) was applied a fault detection of a synchronous generator being superior to a transient state signal analysis and being easy to real time realization. The fault signals after executing a terminal fault modeling collect using a MATLAB package, and calculate the wavelet coefficients through the process of a multi-level decomposition (MLD). The proposed algorithm of a fault detection of a generator using Daubechies WT (wavelet transform) was executed with a C language for the commend line function and for the real time realization after analyzing MATLAB's graphical interface.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.10 s.175
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• pp.80-89
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• 2005

In this paper, time-frequency analysis using wavelet packet transform and advanced-MDSA (Multiple Dimensional Spectral Analysis) which based on wavelet packet transform is applied fur fault source identification and diagnosis of early detection of fault non-stationary sound/vibration signals. This method is analyzing the signal in the plane of instantaneous time and instantaneous frequency. The results of ordinary coherence function, which obtained by wavelet packet analysis, showed the possibility of early fault detection by analysis at the instantaneous time. So, by checking the coherence function trend, it is possible to detect which signal contains the major fault signal and to know how much the system is damaged. Finally, It is impossible to monitor the system is damaged or undamaged by using conventional method, because crest factor is almost constant under the range of magnitude of fault signal as its approach to normal signal. However instantaneous coherence function showed that a little change of fault signal is possible to monitor the system condition. And it is possible to predict the maintenance time by condition based maintenance for any stationary or non-stationary signals.

• Journal of Power Electronics
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• v.19 no.2
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• pp.443-453
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• 2019

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2315-2325
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• 2015

Induction motors are widely used in industrial processes since they offer a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to an unexpected interruption at the industrial plant. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the no-loaded case. To overcome this drawback, this paper deals with an efficient and new method to diagnose the induction-motor rotor fault based on the digital implementation of the monitoring algorithm based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform. Experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the combined “Time Synchronous Averaging – Discrete Wavelet Transform” approach.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.107-116
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• 2003

This paper presents a systematic design methodology for fault tolerant controller against a fault in actuators and sensors of linear stochastic systems with uncertainties. The scheme is based on fault detection and diagnosis(isolation and estimation) using a bank of robust two-stage Kalman filters, and accommodation of the actuator fault by eigenstructure assignment and immediate compensation of the sensor's faulty measurement. In order to clarify the fault feature in test statistics of residual, noise reduction method is given by multi-scale discrete wavelet transform. The effectiveness of our approach Is shown via simulations for a VTOL(vertical take-off and landing) aircraft subjected to parameter variations, external disturbances, process and sensor noises.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.590-595
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• 2001

Vibration transient generated by developing localized fault in gear can be used as indicators of condition monitoring in a gear. In this paper, we propose the phase map for a fault signal using continuous wavelet transform to detect this vibration transient. Local fault induces the abrupt fluctuation of load exciting tooth and phase lag in the vibration signal measured on the gearbox. The relatively large fault like "tip breakage" easily can be detected by the clear fluctuation of exciting load. However, minor fault like "initial pitting" cannot be detected using the load fluctuation. To detect this kind of minor fault, the phase map for a fault signal is taken into account. The phase lag by minor fault is observed well in the phase map.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.10
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• pp.602-609
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• 2003

This paper describes the fault location calculation using neuro-fuzzy systems in combined transmission lines with underground power cables. Neuro-fuzzy systems used in this paper are composed of two parts for fault section and fault location. First, neuro-fuzzy system discriminates the fault section between overhead and underground with normalized detail coefficient obtained by wavelet transform. Normalized detail coefficients of voltage and current in half cycle information are used for the inputs of neuro-fuzzy system. As the result of neuro-fuzzy system for fault section, impedance of selected fault section is calculated and it is used as the inputs of the neuro-fuzzy systems for fault location. Neuro-fuzzy systems for fault location also consist of two parts. One calculates the fault location of overhead, and the other does for underground. Fault section is completely classified and neuro-fuzzy system for fault location calculates the distance from the relaying point. Neuro-fuzzy systems proposed in this paper shows the excellent results of fault section and fault location.