Fatigue feature classification for automotive strain data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIOP Conference Series: Materials Science and Engineering
Volume36
Edition1
DOIs
Publication statusPublished - 2012
Event1st International Conference on Mechanical Engineering Research 2011, ICMER 2011 - Kuantan, Pahang, Malaysia
Duration: 5 Dec 20117 Dec 2011

Other

Other1st International Conference on Mechanical Engineering Research 2011, ICMER 2011
CountryMalaysia
CityKuantan, Pahang
Period5/12/117/12/11

Fingerprint

Fatigue damage
Fatigue of materials
Signal analysis

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

Cite this

Yunoh, M. F. M., Abdullah, S., Mohd Nopiah, Z., Nuawi, M. Z., & Ismail, N. (2012). Fatigue feature classification for automotive strain data. In IOP Conference Series: Materials Science and Engineering (1 ed., Vol. 36). [012031] https://doi.org/10.1088/1757-899X/36/1/012031

Fatigue feature classification for automotive strain data. / Yunoh, M. F M; Abdullah, Shahrum; Mohd Nopiah, Zulkifli; Nuawi, Mohd. Zaki; Ismail, N.

IOP Conference Series: Materials Science and Engineering. Vol. 36 1. ed. 2012. 012031.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yunoh, MFM, Abdullah, S, Mohd Nopiah, Z, Nuawi, MZ & Ismail, N 2012, Fatigue feature classification for automotive strain data. in IOP Conference Series: Materials Science and Engineering. 1 edn, vol. 36, 012031, 1st International Conference on Mechanical Engineering Research 2011, ICMER 2011, Kuantan, Pahang, Malaysia, 5/12/11. https://doi.org/10.1088/1757-899X/36/1/012031
Yunoh MFM, Abdullah S, Mohd Nopiah Z, Nuawi MZ, Ismail N. Fatigue feature classification for automotive strain data. In IOP Conference Series: Materials Science and Engineering. 1 ed. Vol. 36. 2012. 012031 https://doi.org/10.1088/1757-899X/36/1/012031
Yunoh, M. F M ; Abdullah, Shahrum ; Mohd Nopiah, Zulkifli ; Nuawi, Mohd. Zaki ; Ismail, N. / Fatigue feature classification for automotive strain data. IOP Conference Series: Materials Science and Engineering. Vol. 36 1. ed. 2012.
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