• Journal of KSNVE
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• v.10 no.2
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• pp.331-337
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2077-2078
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• 2011

Now a days, the induction motor is widely used in industry automation. Without monitoring the motor fault, maintenance cost is increased undesirably high. The slip frequency is included in the feature frequency, so rotating rotor speed is needed. In this paper, a sensorless motor speed estimation method, rotor slot harmonic(RSH) method is suggested and a solution of rotor bar diagnosis is proposed for motor running with light-load. When the rotor is rotating, it shows the harmonic signal of back-emf voltage related with number of rotor slot. So from the power spectrum of current signal, we can find the rotor speed.

• Journal of Power Electronics
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• v.16 no.4
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• pp.1298-1305
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• 2016

This paper presents the development of a short-circuit protection mechanism on a high voltage switch (HVS) board which is built by a series connection of semiconductor switches. The HVS board is able to quickly detect and limit the peak fault current before the signal board triggers off a gate signal. Voltage clamping techniques are used to safely turn off the short-circuit current and to prevent overvoltage of the series-connected switches. The selection method of the main devices and the development of the HVS board are described in detail. Experimental results have demonstrated that the HVS board is capable of withstanding a short-circuit current at a rated voltage of 10kV without a di/dt slowing down inductor. The corresponding short-circuit current is restricted to 125 A within 100 ns and can safely turn off within 120 ns.

• 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 KIEE Conference
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• 2005.07a
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• pp.211-213
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• 2005

In this paper, the frequency compensator for digital protective relay is introduced. Due to sudden appearance of generation-load or fault in power system, the frequency can deviate from its nominal value. The Orthogonal filter adopted to digital protective relay is exposed to the problem. Therefore, the frequency variation makes a difficult to protective function and power measurement in digital protective relay. The essential property of the proposed algorithm presented is its outstanding immunity to both signal orthogonal component magnitudes and DFT filter gain variations which ensures below 0.05Hz accuracy of estimation. Additionally, the proposed method has excellent characteristics with low signal sampling rate. Based on the frequency estimation, author design under frequency protection relay for generator and verifies its performance with several experimental tests.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.243-245
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• 2005

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. In this paper, a model-based method using neural network modeling of induction noter in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals to continuous spectra so that the neural network model can be trained with vibration spectra. And the faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.517-519
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• 2005

In this paper, we consider a digital protection IED(Intelligent Electric Device) for a distributed power system. The IED can measure various elements for protection and communicate with another devices through network. The protection IED is composed of specific function modules: signal process module which converts analog signal from PT and CT handle algorithm to digital one; communication module for connection with another IEDs; input/output module for user-interfaces. A general purpose DSP board with TMS320C2812 is used in the IED. In order to verify the proposed IED, experimental researches with the power system simulator DOBLE has been carried out for a phase earth fault. The results show an under-voltage relaying algorithm has been realized sucessfully in the hardware system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1102-1107
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• 2000

The signals that can be obtained from a rotating machine often convey the information of machine. For example, if the machine under investigation has faults, then we can measure the signal which has a pulse train, embedded in noise. Therefore the ability to detect the fault signal in noise determines the degree of diagnosis level of rotating machine. In this paper, minimum variance cepstrum (MV cepstrum), which can easily detect impulse in noise, has been applied to detect the type of faults of ball bearing system. To test the performance of this technique, experiment has been performed for ball bearing elements that have man made faults. Results show that minimum variance cepstrum can easily detect the periodicity due to faults.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.969-974
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• 2010

In this paper, we propose a fault-tolerant controller for compensating measurement noise of feedback sensor. Because control systems operate via feedback sensor's signal, the measurement noise in sensor's signal results in performance degradation or even system failure. Therefore, control systems often demand on compensating measurement noise. Our controller is equipped with a compensator in order to reject or reduce the effect of measurement noise in feedback information. Our proposed method is verified via simulation and experiment for a Ball and Beam system.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.7
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• pp.449-453
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• 2016

The amount of electrical energy has been increased with the rapid development of the industrial society. Accordingly, operating voltage of the power equipment and facility capacity are continuously increasing. Development trends of recent high-voltage electrical equipment are ultra high-voltage, large-capacity and compact. Early diagnosis of a failure of the power plant has been emerging as an important task as to supply high quality power to users. In this study, we have tried to develope an algorithm for distinguishing an arc fault signal generated in the power plant by using UV sensor.