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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2012
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/25272/1/Fatigue%20feature%20classification%20for%20automotive%20strain%20data.pdf |
| _version_ | 1796993436248702976 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T12:33:57Z |
| format | Conference or Workshop Item |
| id | UMPir25272 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:33:57Z |
| publishDate | 2012 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | UMPir252722019-11-11T08:52:50Z 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 |
| 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 |
| title | 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_short | Fatigue feature classification for automotive strain data |
| title_sort | fatigue feature classification for automotive strain data |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/25272/1/Fatigue%20feature%20classification%20for%20automotive%20strain%20data.pdf |
| work_keys_str_mv | AT mfmyunoh fatiguefeatureclassificationforautomotivestraindata AT sabdullah fatiguefeatureclassificationforautomotivestraindata AT zmnopiah fatiguefeatureclassificationforautomotivestraindata AT mznuawi fatiguefeatureclassificationforautomotivestraindata AT nurazimaismail fatiguefeatureclassificationforautomotivestraindata |