• Journal of KSNVE
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• v.9 no.2
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• pp.393-400
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• 1999

Defect frequencies of rolling element bearings are experimentally investigated utilizing the two-sided directional spectra of the complex-valued vibration signals measured from the outer ring of defective bearings. The directional spectra make it possible to discern backward and forward defect frequencies. The experimental results show that the directional zoom spectrum is superior to the conventional spectrum in identification of bearing defect frequencies, in particular the inner race defect frequencies.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.10
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• pp.1492-1499
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• 2004

In this paper, defect formation in ring rolling is revealed by computer simulation of ring rolling processes. The rigid-plastic finite element method is employed for this study. An analysis model having relatively fine mesh system near the roll gap is used for reducing the computational time and a scheme of minimizing the volume change is applied. The formation of the central cavity formation defect in ring rolling of a taper roller bearing outer race and the polygonal shape defect in ring rolling of a ball bearing outer race has been simulated. It has been seen that the results are qualitatively good with actual phenomena.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.92-100
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• 1995

In this study, the bearing defects were modeled and the vibration of ball bearing faults was presented for the defective pattern. The vibration signal was measured for the single and multiple defected ball bearing at the various defect positions and rotation speed, and then the signal components using the HFRT(high frequency resonance technique) were analyzed by FFT. The experimental data analysis has shown that the frequencies generated in the single or multiple defected ball bearings appear with the characteristic defect frequency and harmonics of ball pass frequency peak. Signal processing by HFRT makes it possible not only to detect the presence of a defect but also to diagnose the defect part of the bearing.

• Journal of IKEEE
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• v.26 no.2
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• pp.319-328
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• 2022

Bearing plays an important role in the operation of most machinery, Therefore, when a defect occurs in the bearing, a fatal defect throughout the machine is generated. In this reason, bearing defects should be detected early. In this paper, we describe a method using Convolutional Neural Networks (SN-CNNs) based on continuous wavelet transformations and Switchable normalization for bearing defect detection models. The accuracy of the model was measured using the Case Western Reserve University (CWRU) bearing dataset. In addition, batch normalization methods and spectrogram images are used to compare model performance. The proposed model achieved over 99% testing accuracy in CWRU dataset.

• Journal of the Korean Society of Safety
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• v.11 no.2
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• pp.42-51
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• 1996

Taper roller bearing is used on rotating shaft where radial and thrust loads are attended. To avoid the sudden failure and maintain the good condition of rotating machinery it is necessary to monitor the condition of bearing and diagnose the defect of bearing. In this study the diagnosis program of taper roller bearing which is used on the lower bearing of V.A.W.T. (Vertical Axis Wind Turbine) is developed. By plenty of test the database is constructed and by Gaussian distribution obtained from database the defect probability of bearing is calculated.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.46-53
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• 1997

This paper presents a method of detection localized defects on tapered roller bearing in main spindle of machine tool system. The feature vectors, i.e. statistical parameters, in time-domain analysis technique have been calculated to extract useful features from acoustic emission signals. These feature vectors are used as the input feature of an neural network to classify and detect bearing defects. As a results, the detection of bearing defect conditions could be sucessfully performed by using an neural network with statistical parameters of acoustic emission signals.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.451-452
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• 2007

• The Journal of the Acoustical Society of Korea
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• v.16 no.4
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• pp.21-28
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• 1997

In this study, comparison between Harris-theoretical values and experimental data of load-deflection characteristics in bearing was made. The experiments are conducted under the conditions of the various radial loads and speed of shaft. In the case of non-defective ball bearing, the experimental data agreed well with the Harris-theoretical values for the small steady radial load but not for the large steady radial load. For the radial load bearing, the experimental results show that the stiffness of bearing at the single and multiple defective bearing are bigger in the radial defectiion than in the axial deflection. Load-deflection characteristics for the bearing defect part make it possible to detect the presence of a defect in bearing.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.611-616
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• 2002

Bearing diagnostics is difficult in a gearbox because bearing signals are masked by the strong gear signals. Self adaptive noise cancellation(SANC) is useful technique to seperate bearing signals from gear signals. While gear signals are correlated with a long correlation length, bearing signals are not correlated with a short length. SANC seperates two components on the basis of correlation length. Then we can find defect frequency component in the envelope spectrum of the bearing signals.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.5
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• pp.672-678
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• 2009

Acoustic Emission (AE) technique is a non-destructive testing method and widely used for the early detection of faults in rotating machines in these days, because the sensitivity of AE transducers is higher than normal accelerometers. So it can detect low energy vibration signals. The faults in the rotating machines are generally occurred at bearings and gearboxes which are the principal parts of the machines. It was studied to detect the bearing faults by envelop analysis in several decade years. And the researches showed that AE had a possibility of the application in condition monitoring system(CMS) using the envelope analysis for the rolling bearing. And peak ratio (PR) was developed for expression of the bearing condition in condition monitoring system using AE. Noise level is needed to reduce to take exact PR value because the PR is calculated from total root mean square (RMS) and the harmonics peak levels of the defect frequencies of the bearing. Therefore, in this paper, the discrete wavelet transform (DWT) was added in the envelope analysis to reduce the noise level in the AE signals. And then, the PR was calculated and compared with general envelope analysis result and the result of envelope analysis added the DWT. In the experiment result about inner fault of bearing, defect frequency was difficult to find about only envelop analysis. But it's easy to find defect frequency after wavelet transform. Therefore, Envelop analysis added wavelet transform was useful method for early detection of default in signal process.