Time-series identification of fatigue strain data using decomposition method

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the time-series identification a variable-amplitude (VA) strain signal on lower arm suspension component in terms of time-series component analysing and correlate to the fatigue damage properties. The identification technique was used is called classical decomposition method, to classify the strain data into trend, cyclical, seasonal and irregular components. The time history plot of a study case showed the fatigue data contains high and low amplitude events and has resulted the highest amplitude for a pavé, highway and campus are 224 με, 321 με and 619 με, respectively. The trend pattern of a fatigue strain data is a nonstationary series in variance and mean, where a campus data produced highest slope of 31.2×10-4 compared to the others. By observing the cyclic movement of the moving average plot, the fatigue strain data contained expansion, contraction and random background. The autocorrelation plot is weak in identifying seasonal pattern, but the autocorrelation coefficient, r 1 values are statistically significant and show a positive serial correlation. Based on residual plot in irregular analysis, the residuals pattern is considered random. As to correlate the fatigue characteristic and time-series component, it was found a campus data produced highest value of fatigue damage. This study discovered a slope of a linear trend pattern could be affected to the fatigue damage properties because the fatigue strain data are nonstationary, VA time-series data and have a random background. Thus, the findings of these characteristics are expected for a nonstationary signal.

Original languageEnglish
Title of host publicationAIP Conference Proceedings
PublisherAmerican Institute of Physics Inc.
Pages1209-1216
Number of pages8
Volume1602
ISBN (Print)9780735412361
DOIs
Publication statusPublished - 2014
Event3rd International Conference on Mathematical Sciences, ICMS 2013 - Kuala Lumpur
Duration: 17 Dec 201319 Dec 2013

Other

Other3rd International Conference on Mathematical Sciences, ICMS 2013
CityKuala Lumpur
Period17/12/1319/12/13

Fingerprint

fatigue (materials)
decomposition
plots
damage
trends
autocorrelation
slopes
contraction
histories
expansion
coefficients

Keywords

  • decomposition method
  • fatigue
  • strain signal
  • Suspension component
  • time-series analysis

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Mohd Nopiah, Z., Lennie, A., Nuawi, M. Z., Abdullah, S., Ahmat Zainuri, N., & Baharin, M. N. (2014). Time-series identification of fatigue strain data using decomposition method. In AIP Conference Proceedings (Vol. 1602, pp. 1209-1216). American Institute of Physics Inc.. https://doi.org/10.1063/1.4882638

Time-series identification of fatigue strain data using decomposition method. / Mohd Nopiah, Zulkifli; Lennie, A.; Nuawi, Mohd. Zaki; Abdullah, Shahrum; Ahmat Zainuri, Nuryazmin; Baharin, M. N.

AIP Conference Proceedings. Vol. 1602 American Institute of Physics Inc., 2014. p. 1209-1216.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mohd Nopiah, Z, Lennie, A, Nuawi, MZ, Abdullah, S, Ahmat Zainuri, N & Baharin, MN 2014, Time-series identification of fatigue strain data using decomposition method. in AIP Conference Proceedings. vol. 1602, American Institute of Physics Inc., pp. 1209-1216, 3rd International Conference on Mathematical Sciences, ICMS 2013, Kuala Lumpur, 17/12/13. https://doi.org/10.1063/1.4882638
Mohd Nopiah Z, Lennie A, Nuawi MZ, Abdullah S, Ahmat Zainuri N, Baharin MN. Time-series identification of fatigue strain data using decomposition method. In AIP Conference Proceedings. Vol. 1602. American Institute of Physics Inc. 2014. p. 1209-1216 https://doi.org/10.1063/1.4882638
Mohd Nopiah, Zulkifli ; Lennie, A. ; Nuawi, Mohd. Zaki ; Abdullah, Shahrum ; Ahmat Zainuri, Nuryazmin ; Baharin, M. N. / Time-series identification of fatigue strain data using decomposition method. AIP Conference Proceedings. Vol. 1602 American Institute of Physics Inc., 2014. pp. 1209-1216
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AB - This paper presents the time-series identification a variable-amplitude (VA) strain signal on lower arm suspension component in terms of time-series component analysing and correlate to the fatigue damage properties. The identification technique was used is called classical decomposition method, to classify the strain data into trend, cyclical, seasonal and irregular components. The time history plot of a study case showed the fatigue data contains high and low amplitude events and has resulted the highest amplitude for a pavé, highway and campus are 224 με, 321 με and 619 με, respectively. The trend pattern of a fatigue strain data is a nonstationary series in variance and mean, where a campus data produced highest slope of 31.2×10-4 compared to the others. By observing the cyclic movement of the moving average plot, the fatigue strain data contained expansion, contraction and random background. The autocorrelation plot is weak in identifying seasonal pattern, but the autocorrelation coefficient, r 1 values are statistically significant and show a positive serial correlation. Based on residual plot in irregular analysis, the residuals pattern is considered random. As to correlate the fatigue characteristic and time-series component, it was found a campus data produced highest value of fatigue damage. This study discovered a slope of a linear trend pattern could be affected to the fatigue damage properties because the fatigue strain data are nonstationary, VA time-series data and have a random background. Thus, the findings of these characteristics are expected for a nonstationary signal.

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