Fatigue feature classification for automotive strain data

Fatigue strain signal were analysed using data segmentation and data clustering. For data segmentation, value of fatigue damage and global statistical signal analysis such as kurtosis was obtained using specific software. Data clustering were carried out using K-Mean clustering approaches. The objec...

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Main Authors:	M. F. M., Yunoh, S., Abdullah, Z. M., Nopiah, M. Z., Nuawi, Nurazima, Ismail
Format:	Conference or Workshop Item
Language:	English
Published:	IOP Publishing 2012
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/25272/ http://umpir.ump.edu.my/id/eprint/25272/ http://umpir.ump.edu.my/id/eprint/25272/1/Fatigue%20feature%20classification%20for%20automotive%20strain%20data.pdf

id	ump-25272
recordtype	eprints
spelling	ump-252722019-11-11T08:52:50Z http://umpir.ump.edu.my/id/eprint/25272/ Fatigue feature classification for automotive strain data M. F. M., Yunoh S., Abdullah Z. M., Nopiah M. Z., Nuawi Nurazima, Ismail TJ Mechanical engineering and machinery Fatigue strain signal were analysed using data segmentation and data clustering. For data segmentation, value of fatigue damage and global statistical signal analysis such as kurtosis was obtained using specific software. Data clustering were carried out using K-Mean clustering approaches. The objective function was calculated in order to determine the best numbers of groups. This method is used to calculate the average distance of each data in the group from its centroid. Finally, the fatigue failure indexes of metallic components were generated from the best number of group that has been acquired. Based on four data collect from two different roads which are D1, D2, the index value generated is not the same for all of data because due to K-Mean clustering, the best group is different for each of the data used. The maximum indexes generated are different for two types of road and namely the index 4 for D1 and index 5 for D2. Due to the road surface condition, higher distributions of the best groups give higher values of index and reflect to higher fatigue damage experienced by the system. IOP Publishing 2012 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25272/1/Fatigue%20feature%20classification%20for%20automotive%20strain%20data.pdf M. F. M., Yunoh and S., Abdullah and Z. M., Nopiah and M. Z., Nuawi and Nurazima, Ismail (2012) Fatigue feature classification for automotive strain data. In: 1st International Conference on Mechanical Engineering Research, ICMER 2011, 5-7 Disember 2011 , Kuantan, Pahang Darul Makmur. pp. 1-8., 36 (1). ISSN 1757-899X https://doi.org/10.1088/1757-899X/36/1/012031
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 M. F. M., Yunoh S., Abdullah Z. M., Nopiah M. Z., Nuawi Nurazima, Ismail Fatigue feature classification for automotive strain data
description	Fatigue strain signal were analysed using data segmentation and data clustering. For data segmentation, value of fatigue damage and global statistical signal analysis such as kurtosis was obtained using specific software. Data clustering were carried out using K-Mean clustering approaches. The objective function was calculated in order to determine the best numbers of groups. This method is used to calculate the average distance of each data in the group from its centroid. Finally, the fatigue failure indexes of metallic components were generated from the best number of group that has been acquired. Based on four data collect from two different roads which are D1, D2, the index value generated is not the same for all of data because due to K-Mean clustering, the best group is different for each of the data used. The maximum indexes generated are different for two types of road and namely the index 4 for D1 and index 5 for D2. Due to the road surface condition, higher distributions of the best groups give higher values of index and reflect to higher fatigue damage experienced by the system.
format	Conference or Workshop Item
author	M. F. M., Yunoh S., Abdullah Z. M., Nopiah M. Z., Nuawi Nurazima, Ismail
author_facet	M. F. M., Yunoh S., Abdullah Z. M., Nopiah M. Z., Nuawi Nurazima, Ismail
author_sort	M. F. M., Yunoh
title	Fatigue feature classification for automotive strain data
title_short	Fatigue feature classification for automotive strain data
title_full	Fatigue feature classification for automotive strain data
title_fullStr	Fatigue feature classification for automotive strain data
title_full_unstemmed	Fatigue feature classification for automotive strain data
title_sort	fatigue feature classification for automotive strain data
publisher	IOP Publishing
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/25272/ http://umpir.ump.edu.my/id/eprint/25272/ http://umpir.ump.edu.my/id/eprint/25272/1/Fatigue%20feature%20classification%20for%20automotive%20strain%20data.pdf
first_indexed	2023-09-18T22:38:43Z
last_indexed	2023-09-18T22:38:43Z
_version_	1777416770504622080

Fatigue feature classification for automotive strain data

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