### 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 language | English |
---|---|

Pages (from-to) | 91-100 |

Number of pages | 10 |

Journal | WSEAS Transactions on Signal Processing |

Volume | 6 |

Issue number | 3 |

Publication status | Published - Jul 2010 |

### Fingerprint

### 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

*WSEAS Transactions on Signal Processing*,

*6*(3), 91-100.

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

Research output: Contribution to journal › Article

*WSEAS Transactions on Signal Processing*, vol. 6, no. 3, pp. 91-100.

}

TY - JOUR

T1 - Extracting fatigue damage features using STFT and CWT

AU - Abdullah, Shahrum

AU - Putra, T. E.

AU - Nuawi, Mohd. Zaki

AU - Mohd Nopiah, Zulkifli

AU - Arifin, Azli

AU - Abdullah, L.

PY - 2010/7

Y1 - 2010/7

N2 - 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.

AB - 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.

KW - Edited signal

KW - Fatigue damage

KW - Fatigue strain signal

KW - Morlet wavelet

KW - STFT

UR - http://www.scopus.com/inward/record.url?scp=77955120831&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77955120831&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:77955120831

VL - 6

SP - 91

EP - 100

JO - WSEAS Transactions on Signal Processing

JF - WSEAS Transactions on Signal Processing

SN - 1790-5022

IS - 3

ER -