A Wavelet Decomposition Analysis of Vibration Signal for Bearing Fault Detection

This paper presents the study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data were acquired from three different type of bearing defect i.e. corroded, outer race defect and point defect. The experimental...

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Main Authors:	Che Ku Eddy Nizwan, Che Ku Husin, S. A., Ong, Mohd Fadhlan, Mohd Yusof, Mohamad Zairi, Baharom
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/3922/ http://umpir.ump.edu.my/id/eprint/3922/1/P226.pdf

id	ump-3922
recordtype	eprints
spelling	ump-39222018-03-01T08:00:43Z http://umpir.ump.edu.my/id/eprint/3922/ A Wavelet Decomposition Analysis of Vibration Signal for Bearing Fault Detection Che Ku Eddy Nizwan, Che Ku Husin S. A., Ong Mohd Fadhlan, Mohd Yusof Mohamad Zairi, Baharom TJ Mechanical engineering and machinery This paper presents the study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data were acquired from three different type of bearing defect i.e. corroded, outer race defect and point defect. The experimental were carried out at three different speeds which are 10%, 50% and 90% of the maximum motor speed. The time domain vibration data measured from accelerometer was then transform into frequency domain using frequency analyzer in order to study the frequency characteristics of the signal. The DWT was utilized to decomposed signal at different frequency scale. Then, root mean square (RMS) for every decomposition level were calculated to detect the defect features in vibration signals by referring to the trend of vibrational energy retention at every decomposition. Based on the result, the defective bearings show the significant deviation in retaining RMS value after a few level of decomposition. The findings indicate that Wavelet decomposition analysis can be used to develop an effective bearing condition monitoring tool. This signal processing analysis is recommended to use in on-line monitoring while the machine is on operation. 2013-07-01 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/3922/1/P226.pdf Che Ku Eddy Nizwan, Che Ku Husin and S. A., Ong and Mohd Fadhlan, Mohd Yusof and Mohamad Zairi, Baharom (2013) A Wavelet Decomposition Analysis of Vibration Signal for Bearing Fault Detection. In: International Conference on Mechanical Engineering Research (ICMER2013), 1-3 July 2013 , Bukit Gambang Resort City, Kuantan, Pahang, Malaysia. pp. 1-9.. (Unpublished)
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 Che Ku Eddy Nizwan, Che Ku Husin S. A., Ong Mohd Fadhlan, Mohd Yusof Mohamad Zairi, Baharom A Wavelet Decomposition Analysis of Vibration Signal for Bearing Fault Detection
description	This paper presents the study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data were acquired from three different type of bearing defect i.e. corroded, outer race defect and point defect. The experimental were carried out at three different speeds which are 10%, 50% and 90% of the maximum motor speed. The time domain vibration data measured from accelerometer was then transform into frequency domain using frequency analyzer in order to study the frequency characteristics of the signal. The DWT was utilized to decomposed signal at different frequency scale. Then, root mean square (RMS) for every decomposition level were calculated to detect the defect features in vibration signals by referring to the trend of vibrational energy retention at every decomposition. Based on the result, the defective bearings show the significant deviation in retaining RMS value after a few level of decomposition. The findings indicate that Wavelet decomposition analysis can be used to develop an effective bearing condition monitoring tool. This signal processing analysis is recommended to use in on-line monitoring while the machine is on operation.
format	Conference or Workshop Item
author	Che Ku Eddy Nizwan, Che Ku Husin S. A., Ong Mohd Fadhlan, Mohd Yusof Mohamad Zairi, Baharom
author_facet	Che Ku Eddy Nizwan, Che Ku Husin S. A., Ong Mohd Fadhlan, Mohd Yusof Mohamad Zairi, Baharom
author_sort	Che Ku Eddy Nizwan, Che Ku Husin
title	A Wavelet Decomposition Analysis of Vibration Signal for Bearing Fault Detection
title_short	A Wavelet Decomposition Analysis of Vibration Signal for Bearing Fault Detection
title_full	A Wavelet Decomposition Analysis of Vibration Signal for Bearing Fault Detection
title_fullStr	A Wavelet Decomposition Analysis of Vibration Signal for Bearing Fault Detection
title_full_unstemmed	A Wavelet Decomposition Analysis of Vibration Signal for Bearing Fault Detection
title_sort	wavelet decomposition analysis of vibration signal for bearing fault detection
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/3922/ http://umpir.ump.edu.my/id/eprint/3922/1/P226.pdf
first_indexed	2023-09-18T21:58:30Z
last_indexed	2023-09-18T21:58:30Z
_version_	1777414240035930112

A Wavelet Decomposition Analysis of Vibration Signal for Bearing Fault Detection

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