The Morlet wavelet analysis for fatigue feature clustering

Authors:
S. Abdullah;T. E. Putra;M. Z. Nuawi;Z. M. Nopiah;A. Arifin;L. Abdullah
Affiliations:
Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
Venue:
WSEAS Transactions on Mathematics
Year:
2010

Citing 5
Cited 0

Application of a computational data editing algorithm to summarise fatigue road loadings

CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
Fatigue life assessment using signal processing techniques

ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
Peak-valley segmentation algorithm for fatigue time series data

WSEAS Transactions on Mathematics
Abrupt changes detection in fatigue data using the cum ulative sum method

WSEAS Transactions on Mathematics
Localization of the complex spectrum: the S transform

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

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.