Bearing fault detection using discrete wavelet transform

Rolling element bearing has vast domestic and industrial applications. Appropriate function of these appliances depends on the smooth operation of the bearings. Result of various studies shows that bearing problems account for over 40% of all machine failures. Therefore this research is to design a...

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Main Author:	Syahril Azeem Ong, Haji Maliki Ong
Format:	Undergraduates Project Papers
Language:	English
Published:	2012
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/4504/ http://umpir.ump.edu.my/id/eprint/4504/ http://umpir.ump.edu.my/id/eprint/4504/1/cd6600_103.pdf

id	ump-4504
recordtype	eprints
spelling	ump-45042015-03-03T09:19:23Z http://umpir.ump.edu.my/id/eprint/4504/ Bearing fault detection using discrete wavelet transform Syahril Azeem Ong, Haji Maliki Ong TJ Mechanical engineering and machinery Rolling element bearing has vast domestic and industrial applications. Appropriate function of these appliances depends on the smooth operation of the bearings. Result of various studies shows that bearing problems account for over 40% of all machine failures. Therefore this research is to design a test rig to harness data in terms of types of defects and rotation speed and also to develop method to detect features in vibration signals. Six set of bearings were tested with one of them remains in good condition while the other five has its own type of defects have been considered for analysis by using Discrete Wavelet Transform (DWT). The data for a good bearing were used as benchmark to compare with the defective ones. MATLAB’s Discrete Wavelet Transform ToolBox was used to down-sample the vibration signals into noticeable form to detect defect features under certain frequency with respect to time. From the result generated, Fast Fourier Transform (FFT) and Root Mean Square (RMS) plays an important role in supporting results analyzed by using DWT from MATLAB® Toolbox. A system with low operating speed yields unsystematic results due to low excitation. As the speed increases, the excitation increases thus making DWT works effectively. Fordata of insufficient excitation, defect features still may be discovered by calculating and plotting graph for the percentage of RMS value of each decomposition level compared to the original input. This shows that DWT appears to be effective in pointing out the location and frequency of defect when the excitation is high enough. If the excitation is low, RMS value of each decomposition level may support the result. Nevertheless, DWT also proves to be an effective method for online condition monitoring tool. Future research should be detecting defect features by using envelope analysis or based on statistical tools. 2012-06 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/4504/1/cd6600_103.pdf Syahril Azeem Ong, Haji Maliki Ong (2012) Bearing fault detection using discrete wavelet transform. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:71778&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Syahril Azeem Ong, Haji Maliki Ong Bearing fault detection using discrete wavelet transform
description	Rolling element bearing has vast domestic and industrial applications. Appropriate function of these appliances depends on the smooth operation of the bearings. Result of various studies shows that bearing problems account for over 40% of all machine failures. Therefore this research is to design a test rig to harness data in terms of types of defects and rotation speed and also to develop method to detect features in vibration signals. Six set of bearings were tested with one of them remains in good condition while the other five has its own type of defects have been considered for analysis by using Discrete Wavelet Transform (DWT). The data for a good bearing were used as benchmark to compare with the defective ones. MATLAB’s Discrete Wavelet Transform ToolBox was used to down-sample the vibration signals into noticeable form to detect defect features under certain frequency with respect to time. From the result generated, Fast Fourier Transform (FFT) and Root Mean Square (RMS) plays an important role in supporting results analyzed by using DWT from MATLAB® Toolbox. A system with low operating speed yields unsystematic results due to low excitation. As the speed increases, the excitation increases thus making DWT works effectively. Fordata of insufficient excitation, defect features still may be discovered by calculating and plotting graph for the percentage of RMS value of each decomposition level compared to the original input. This shows that DWT appears to be effective in pointing out the location and frequency of defect when the excitation is high enough. If the excitation is low, RMS value of each decomposition level may support the result. Nevertheless, DWT also proves to be an effective method for online condition monitoring tool. Future research should be detecting defect features by using envelope analysis or based on statistical tools.
format	Undergraduates Project Papers
author	Syahril Azeem Ong, Haji Maliki Ong
author_facet	Syahril Azeem Ong, Haji Maliki Ong
author_sort	Syahril Azeem Ong, Haji Maliki Ong
title	Bearing fault detection using discrete wavelet transform
title_short	Bearing fault detection using discrete wavelet transform
title_full	Bearing fault detection using discrete wavelet transform
title_fullStr	Bearing fault detection using discrete wavelet transform
title_full_unstemmed	Bearing fault detection using discrete wavelet transform
title_sort	bearing fault detection using discrete wavelet transform
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/4504/ http://umpir.ump.edu.my/id/eprint/4504/ http://umpir.ump.edu.my/id/eprint/4504/1/cd6600_103.pdf
first_indexed	2023-09-18T21:59:09Z
last_indexed	2023-09-18T21:59:09Z
_version_	1777414281121234944

Bearing fault detection using discrete wavelet transform

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