• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.43-48
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• 2008

Gear system has been widely used in industrial applications and unexpected failures of gears are not only extremely damaging but also lead to economic losses. So, early detection of fault is important for diagnosis machine condition. And acoustic emission is an efficient non destructive testing technique for the diagnosis of machine health and is useful technique for early detection of fault because it can find low-amplitude and high-frequency signal on account of high sensibility. Therefore, in this paper, the AE signal was measured and preprocessed using envelop analysis for gearbox with misalignment between pinion and gear. And then the vibration characteristic of gear misalignment was analyzed.

• 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

• 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.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.301-305
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• 2001

A novel approach based on parameters estimation is presented far fault detection and diagnosis in sensors. Based on known precise parameter of normal working sensors system model is built from real laboratory inputs-outputs data, sequentially residual serial is obtained. Where decision-making rule of detection the fault is given via the use of beys theory, whilst a filter least-square computative algorithm for estimating fault parameters is given. The algorithm is a fast and accurate to calculate value of sensors faults when system model contains noise and sensors outputs contain measured noise. The method can solve both gain type and bias type fault in sensors. Simulated numerical example is included to demonstrate the use of the proposed approaches.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.11
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• pp.1091-1100
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• 2004

This paper presents an approach for the monitoring and detection of faults in induction machine by using data fusion technique and Dempster-Shafer theory Features are extracted from motor stator current and vibration signals. Neural network is trained and Hosted by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electric and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real time application.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.6
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• pp.619-623
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• 2008

As higher reliability and accuracy on production facilities are required to detect incipient faults, a diagnostic system for predictive maintenance of the facility is highly recommended. In this paper, it presents a study on the application of vibration signals to diagnose faults for a rotating machinery using the Mahalanobis distance-Taguchi system. RMS, crest factor and Kurtosis that is known as the statistical methods and the spectrum analysis are used to diagnose faults as parameters of Mahalanobis distance.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.986-989
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• 2008

Recently the agitator are being widely used in the machine plan in order to increase the petrochemical industry. The agitator normally consist of impeller, shaft, hub, reduction gear and the driving motor. It is one of the key design issue to confirm that the vibration caused by the rotation of the shaft should not coincide with the natural frequency of the shaft itself. And petrochemical industry as well as plants have been in operation for long period beyond their original design lives. In this paper the vibration of Ox-Reactor Agitator is measured for check machine condition. The result of diagnosis and solution is discussed in this paper.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.7 s.100
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• pp.781-787
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• 2005

The correlation dimension can provide some intrinsic Information of an underlying dynamic system by reconstructing measured nonlinear time series. The vibration signals measured from a rotor with different clearance sizes between shaft and bushing were analyzed using the correlation dimension. The results showed that the correlation dimension can identify the size of the clearance of a rotor and the lubricating condition, which can not be analyzed by frequency spectrum or wavelet. The magnitude of the correlation dimension became smaller as the clearance larger and as the lubrication condition better.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.979-984
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• 2004

The ball bearing having faults generally shows, nonlinear vibration characteristics. For the effective method of fault diagnosis on bail bearing, non-linear diagnostic methods can be used. In this paper, the correlation dimension analysis based on nonlinear timeseries was applied to diagnose the faults of ball bearing. The correlation dimension analysis shows some Intrinsic information of underlying dynamical systems, and clear the classification of the fault of ball bearing.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1127-1132
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• 2000

Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. The detection of these impulses can be useful for fault diagnosis purposes. Recently there has been an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. This paper considers the use of the third and fourth order Wigner moment spectra, called the Wigner bi- and tri- spectra receptively, for analysing such signals. Expressions for the auto-and cross-terms in these distributions are presented and discussed. It is shown that the Wigner trispectrum is a more suitable analysis tool and it performance is compared to its second order counterpart for detecting impulsive signals. These methods are also applied to measured data sets from an industrial gearbox.