Extracting fatigue damage features using STFT and CWT

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The fatigue feature extraction using the Short-Time Fourier Transform (STFT) and wavelet transform approaches are presented in this paper. The transformation of the time domain signal into time-frequency domain computationally implemented using the STFT and Morlet wavelet methods provided the signal energy distribution display with respect to the particular time and frequency information. In this study, cycles with lower energy content were eliminated, and these selections were based on the signal energy distribution in the time representation. The simulation results showed that the Morlet wavelet was found to be a better approach for fatigue feature extraction. The wavelet-based analysis obtained a 59 second edited signal with the retention of at least 94 % of the original fatigue damage. The edited signal was 65 seconds (52 %) shorter than length of the edited signal that was found using the STFT approach. Hence, this fatigue data summarising algorithm can be used for accelerating the simulation works related to fatigue durability testing.

Original languageEnglish
Pages (from-to)91-100
Number of pages10
JournalWSEAS Transactions on Signal Processing
Volume6
Issue number3
Publication statusPublished - Jul 2010

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Fatigue damage
Fourier transforms
Fatigue of materials
Feature extraction
Wavelet transforms
Durability
Display devices
Testing

Keywords

  • Edited signal
  • Fatigue damage
  • Fatigue strain signal
  • Morlet wavelet
  • STFT

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Extracting fatigue damage features using STFT and CWT. / Abdullah, Shahrum; Putra, T. E.; Nuawi, Mohd. Zaki; Mohd Nopiah, Zulkifli; Arifin, Azli; Abdullah, L.

In: WSEAS Transactions on Signal Processing, Vol. 6, No. 3, 07.2010, p. 91-100.

Research output: Contribution to journalArticle

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