• Proceedings of the KSR Conference
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• 2003.10c
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• pp.529-534
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• 2003

Within industry induction motors have a broad application area to drive pumps, fans, elevators and electric trains. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Journal of Power System Engineering
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• v.9 no.2
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• pp.45-49
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• 2005

Liquefied natural gas takes up six hundredths of the volume of natural gas, which makes storage and transportation much easier. To send out natural gas via a pipeline network across the nation, high-pressure LNG pumps supply highly compressed LNG to high-pressure vaporization facilities. The Number of high-pressure LNG pumps determined the send-out amount in LNG receiving terminal. So it is main equipment at LNG production process and should be maintained on best conditions. In this paper, to find out the cause of high beat vibration at cryogenic pumps, vibration and motor current signal analysis have been performed. High vibration of cryogenic pumps could be reduced due to the modification of motor rotor.

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.929-940
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• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3512-3517
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• 2007

Nuclear power plant has many external fire hydrants that have to operate in the state of emergency such as facility fire, forest fire. The valve stem of one among them was broken 3 times for 4 years. It had long valve stem and operated under high water pressure. The elongation and the tensile strength for the broken valve stem was measured to examine the defect of material property. And the vibration level and the natural frequencies was detected to check the resonance. As the result of a diagnosis, the cause of this fault is proven buckling of long valve stem.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.407-410
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• 2006

Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine teaming, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for four data sets with good performance results

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.75-83
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• 2004

With the rotating machinery getting more accurate and diversified, the necessity fur an appropriate diagnosis technique and maintenance system has been greatly recognized. However, until now, the operator has executed a monitoring of the machine by the senses or simple the change of RMS (root mean Square) value. So, the diagnostic expert system using the fuzzy inference which the operator can judge easily and expertly a condition of the machine is developed through this study. In this paper, the hardware and software of the diagnostic expert system was composed and the identification of the diagnostic performance of the developed system for 5 fault phenomena was carried out.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.630-635
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• 2002

This work is concerned with the damage size estimation by using propagating bending wave signals in a beam. For the accurate estimation, we apply the continuous wavelet transforms to the incident waves and the reflected waves from a small damage in a long cylindrical beam. In particular, we propose to use the ratio of the magnitude of the incident and reflected waves along the ridges in the wavelet-transformed time-frequency plane. This technique is applied to the signals measured by non-contact magnetostrictive sensors. Experimental results indicate that the present method using the magnetostrictive sensor can be quite effective for accurate damage size estimation with simple measurements.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.2
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• pp.36-40
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• 2006

Diagnosis and repair tasks of an unbalanced rigid rotor reduce the chances of unexpected failure and the consequent losses in production, time, and money. This paper presents investigation of a balancing system for equilibration of rigid rotor unbalance. A practical vibration signal based method is developed for unbalance diagnosis using wavelet technology and a Lissajous diagram. This paper shows that a mass unbalance can be efficiently estimated through an appropriate narrow-band filter used to extract the required spectra component. The wavelet technology is used to design specified narrow filter bank. A modified Lissajous diagram is also introduced with statistical analysis to compute the phase position. Several experimental tests demonstrate the effectiveness in balancing the mass unbalance of a rigid rotor.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.1
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• pp.48-55
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• 2003

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.839-841
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• 2014

The Nuclear Steam Supply System(NSSS) is designed to provide an integrated approach that includes areas of monitoring relevant to the integrity of the NSSS. LPMS is designed to function as an alarm system by providing sensor channel alarms for the associated subsystems. LPMS is equipped to provide analysis tools for new alarm events, historical events and for historical periodically stored channel data (e.g. waveforms) for most channels. This paper is intended to introduce the diagnosis principle and abnormal symptom of loose parts monitoring system as a monitoring tool in Nuclear Steam Supply System. And also, we are going to introduce signal analysis program in order to perform the actual diagnosis in power plants.