• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11b
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• pp.200-203
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• 2005

Ball bearings not only sustain the system, but permit the rotational component to rotate. Excessive radial or axial load and many other reasons can cause faults to be created and grown rapidly in each component. The grown faults make noise and vibration, which can make the system unstable. Therefore, it is important to detect faults as early as possible. For this reason, there have been many researches on fault detection method of early faults in a ball bearing. The fault defection methods can be categorized to several groups by signal processing methods. Not all the methods are efficient for finding early faults. We select representative methods known as efficient for detecting early faults and compare the results for inspecting which method is effective.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1385-1398
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• 2016

A pressurizer is a major equipment system in a nuclear power plant (NPP) and controls the reactor cooling system pressure within the allowable range. Faults in the pressurizer can be critical to the NPP; therefore, early fault detection in the pressurizer is significant for NPP safety. This study applies Mahalanobis Taguchi system (MTS), which is one of the promising pattern classification methods, based on the Mahalanobis distance concept and Taguchi quality engineering theory to the early fault detection problem of the pressurizer pressure control system. We conducted experiments using data from full scope NPP simulator based on a pressurizer pressure transmitter faults scenario to validate the faults detection performance of MTS. As a result, MTS can rapidly detect the faults compared to conventional faults detection based on single sensor monitoring.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.1093-1096
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• 2004

Bearings are elementary machinery component which loads and do rotating motion. Excessive loads or many other reasons can cause incipient faults to be created and grown in each component. Moreover, it happens that incipient faults which were caused by manufacturing or assembling process' errors of the bearings are created. Finding the incipient faults as early as possible is necessary to the bearings in severe condition: high speed or frequently varying load condition, etc. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing fault signal makes periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1287-1294
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• 2004

Hub bearings not only sustain the body of a cat, but permit wheels to rotate freely. Excessive radial or axial load and many other reasons can cause defects to be created and grown in each component. Therefore, nitration and noise from unwanted defects in outer-race, inner-race or ball elements of a Hub bearing are what we want to detect as early as possible. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing signal has Periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.593-596
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• 2004

Hub bearings not only sustain the body of a car, but permit wheels to rotate freely. Excessive radial or axial load and many other reasons can cause defects to be created and grown in each component. Therefore, vibration and noise from unwanted defects in outer-race, inner-race or ball elements of a Hub bearing are what we want to detect as early as possible. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing signal has periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• 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.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.763-768
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• 2006

The two-for-one(TFO) twister is precision machinery that twists fibers rapidly under constant tension. Since the quality of the twisted yarn is directly deteriorated by faults of the twister, such as the distortion of the spinning axis, bearing abrasion, and tension irregularity, it is important to detect faults of the TFO twister at an early stage. In this research, a new algorithm is proposed to detect faults of the TFO twister and their causes, by measuring the vibrations of the TFO twister and obtaining frequency components with a FFT algorithm. The TFO twister with faults showed increased vibrations and each fault generated vibrations at different frequencies. By analyzing changes of characteristics of vibrations, we can determine faulty twisters. The proposed fault detection algorithm can be implemented cheaply with a signal processor chip. It can be used to find when to repair a faulty TFO twister without much loss of yam on-line.

• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.84-88
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• 2008

The roller bearings play an important role not only sustain radial or axial load of system, but carry out a rotatory movement as a various operating conditions. They happen that incipient faults which were caused by excessive load, manufacturing or assembling process's errors and many other reasons are created. The bearing faults make noise and vibration by a continuous collision of rotatory components, which can lower the quality and stability of auto-transmission. Therefore, it is important to detect the early fault as soon as possible. This paper presents a detecting method for the improvement in quality by developing the program which can be used to analyze and predict the vibrational characteristics caused by roller bearing faults. We completed development of the inspection system of vibration by appling the most efficient detecting methods and verified the system's reliability through experiments.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.3
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• pp.268-273
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• 2009

The roller bearings play an important role not only sustain radial or axial load of system, but carry out a rotatory movement as a various operating conditions. They happen that incipient faults which were caused by excessive load, manufacturing or assembling process's errors and many other reasons are created. The bearing faults make noise and vibration by a continuous collision of rotatory components, which can lower the quality and stability of auto-transmission. Therefore, it is important to detect the early fault as soon as possible. This paper presents a detecting method for the improvement in quality by developing the program which can be used to analyze and predict the vibrational characteristics caused by roller bearing faults. We completed development of the inspection system of vibration by applying the most efficient detecting methods and verified the system's reliability through experiments.