The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SIGNAL PROCESSING
Transactions ID Number: 89-557
Full Name: Shahrum Abdullah
Position: Associate Professor
Age: ON
Sex: Male
Address: Department of Mechanical and Materials Engineering Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor
Country: MALAYSIA
Tel: +60173309669
Tel prefix:
Fax:
E-mail address: shahrum@vlsi.eng.ukm.my
Other E-mails: shahrum1@gmail.com
Title of the Paper: extracting fatigue damage features using stft and cwt
Authors as they appear in the Paper: Shahrum Abdullah, Teuku Edisah Putra, Mohd. Zaki Nuawi, Zulkifli Mohd. Nopiah, Azli Arifin and Lenny Abdullah
Email addresses of all the authors: shahrum@vlsi.eng.ukm.my, edi_unsyiah@yahoo.co.id, zaki@vlsi.eng.ukm.my, zmn@vlsi.eng.ukm.my, azli@eng.ukm.my, lenny_abdul@yahoo.com
Number of paper pages: 10
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 use!
d for accelerating the simulation works related to fatigue durability testing
Keywords: Fatigue strain signal, Fatigue damage, STFT, Morlet wavelet, Edited signal.
EXTENSION of the file: .pdf
Special (Invited) Session: Time-Frequency Localisation Analysis for Extracting Fatigue Damaging Events
Organizer of the Session: 640-172
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