The morlet wavelet analysis for fatigue feature clustering

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This paper presents clustering of fatigue features resulted from the segmentation of SAESUS time series data. The segmentation process was based on the Morlet wavelet coefficient amplitude level which produced 49 segments that each has overall fatigue damage. Observation of the fatigue damage and the wavelet coefficients was made on each segment. At the end of the process, the segments were clustered into three in order to identify any improvements in the data scattering for fatigue data clustering prospects. This algorithm produced a more reliable and suitable method of segment by segment analysis for fatigue strain signal segmentation. According to the findings, the higher Morlet wavelet coefficient presented damaging segment, otherwise, it was non-damaging segment. This indicated that the relationship between the Morlet wavelet coefficient and the fatigue damage was strong and parallel.

Original languageEnglish
Pages (from-to)345-354
Number of pages10
JournalWSEAS Transactions on Mathematics
Volume9
Issue number5
Publication statusPublished - May 2010

Fingerprint

Wavelet Analysis
Wavelet analysis
Fatigue damage
Fatigue
Cluster Analysis
Clustering
Fatigue of materials
Wavelet Coefficients
Fatigue Damage
Segmentation
Time series
Scattering
Data Clustering
Time Series Data
Observation
Wavelets
Coefficients

Keywords

  • Clustering
  • Fatigue damage
  • Fatigue strain signal
  • Morlet wavelet coefficient
  • Segmentation

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

The morlet wavelet analysis for fatigue feature clustering. / Abdullah, Shahrum; Putra, T. E.; Nuawi, Mohd. Zaki; Mohd Nopiah, Zulkifli; Arifin, Azli; Abdullah, L.

In: WSEAS Transactions on Mathematics, Vol. 9, No. 5, 05.2010, p. 345-354.

Research output: Contribution to journalArticle

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