Clustering of frequency-based vibration signal for bearing fault detection

Bearing is one of the vital parts in any rotating machinery. Failure of this particular part can affect the machinery performance and in time will cause major failure to the machinery. Due to this crucial problem, on-line monitoring has become an alternative in prevention maintenance. The objective...

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Main Author:	Chia, Ming Xuan
Format:	Undergraduates Project Papers
Language:	English English English English
Published:	2013
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/8270/ http://umpir.ump.edu.my/id/eprint/8270/ http://umpir.ump.edu.my/id/eprint/8270/1/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28Table%20of%20content%29.pdf http://umpir.ump.edu.my/id/eprint/8270/6/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28Abstract%29.pdf http://umpir.ump.edu.my/id/eprint/8270/7/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28Chapter%201%29.pdf http://umpir.ump.edu.my/id/eprint/8270/14/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28References%29.pdf

id	ump-8270
recordtype	eprints
spelling	ump-82702017-04-04T02:16:52Z http://umpir.ump.edu.my/id/eprint/8270/ Clustering of frequency-based vibration signal for bearing fault detection Chia, Ming Xuan TJ Mechanical engineering and machinery Bearing is one of the vital parts in any rotating machinery. Failure of this particular part can affect the machinery performance and in time will cause major failure to the machinery. Due to this crucial problem, on-line monitoring has become an alternative in prevention maintenance. The objective of this project is to study the trend of frequency spectrum from different bearing defects and to apply clustering approach using Principle Component Analysis, PCA on frequency domain signals. A set of good condition bearing is used along with four types of defective bearing which are inner race defect,corroded defect, contaminated defect and lastly roller defect. The signals are acquired using a PCB piezoelectric accelerometer and a National Instrument Data Acquisition System (NI-DAQ). The bearing will be run on three speed rotation which is 440, 1480 and 2672 RPM. The data is acquired by using DASYLab software, for both the time domain and frequency domain signals. The data then analyzed using PCA method through MATLAB software. Data is then plotted on scatter plot. After that, the data will be clustered using Agglomerative Hierarchical Clustering where a dendrogram is used to show a cluster of data in which the respective data for all types of bearing tested remain in their cluster. Finally, this method is suggested as an alternative in bearing fault detection, especially online monitoring. 2013-06 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8270/1/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28Table%20of%20content%29.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/8270/6/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28Abstract%29.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/8270/7/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28Chapter%201%29.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/8270/14/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28References%29.pdf Chia, Ming Xuan (2013) Clustering of frequency-based vibration signal for bearing fault detection. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:81898&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English English English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Chia, Ming Xuan Clustering of frequency-based vibration signal for bearing fault detection
description	Bearing is one of the vital parts in any rotating machinery. Failure of this particular part can affect the machinery performance and in time will cause major failure to the machinery. Due to this crucial problem, on-line monitoring has become an alternative in prevention maintenance. The objective of this project is to study the trend of frequency spectrum from different bearing defects and to apply clustering approach using Principle Component Analysis, PCA on frequency domain signals. A set of good condition bearing is used along with four types of defective bearing which are inner race defect,corroded defect, contaminated defect and lastly roller defect. The signals are acquired using a PCB piezoelectric accelerometer and a National Instrument Data Acquisition System (NI-DAQ). The bearing will be run on three speed rotation which is 440, 1480 and 2672 RPM. The data is acquired by using DASYLab software, for both the time domain and frequency domain signals. The data then analyzed using PCA method through MATLAB software. Data is then plotted on scatter plot. After that, the data will be clustered using Agglomerative Hierarchical Clustering where a dendrogram is used to show a cluster of data in which the respective data for all types of bearing tested remain in their cluster. Finally, this method is suggested as an alternative in bearing fault detection, especially online monitoring.
format	Undergraduates Project Papers
author	Chia, Ming Xuan
author_facet	Chia, Ming Xuan
author_sort	Chia, Ming Xuan
title	Clustering of frequency-based vibration signal for bearing fault detection
title_short	Clustering of frequency-based vibration signal for bearing fault detection
title_full	Clustering of frequency-based vibration signal for bearing fault detection
title_fullStr	Clustering of frequency-based vibration signal for bearing fault detection
title_full_unstemmed	Clustering of frequency-based vibration signal for bearing fault detection
title_sort	clustering of frequency-based vibration signal for bearing fault detection
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/8270/ http://umpir.ump.edu.my/id/eprint/8270/ http://umpir.ump.edu.my/id/eprint/8270/1/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28Table%20of%20content%29.pdf http://umpir.ump.edu.my/id/eprint/8270/6/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28Abstract%29.pdf http://umpir.ump.edu.my/id/eprint/8270/7/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28Chapter%201%29.pdf http://umpir.ump.edu.my/id/eprint/8270/14/Clustering%20of%20frequency-based%20vibration%20signal%20for%20bearing%20fault%20detection%20%28References%29.pdf
first_indexed	2023-09-18T22:05:39Z
last_indexed	2023-09-18T22:05:39Z
_version_	1777414690433925120

Clustering of frequency-based vibration signal for bearing fault detection

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