Time series behaviour of lower arm suspension fatigue data using classical decomposition method

Zulkifli Mohd Nopiah, M. N. Baharin, Shahrum Abdullah, M. I. Khairir

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

1 Citation (Scopus)

Abstract

The study of time series behaviour refers to the analysis of certain unique attributes that exist in the time series data. The presence of these attributes in the data series may influence the decision making process. These attributes are generally grouped into four main component types which are trend, cyclical, seasonal and irregular components. In this study, fatigue signal data with three different road factors from a lower arm suspension for a mid-sized car were used as the case study. "Classical decomposition" time series method was used to segregate and to analyse the existence components in a systematic manner. Although fatigue data is a time series signal, not all components were considered. This is due to the nature of fatigue behaviour itself which is different from a normal time series data. From the study, it was found that only trend, cyclical and irregular component existed in the fatigue data signal. The study also revealed the additive effect that existed between these three types components as the absolute sizes of the seasonal variation are independent of each other.

Original languageEnglish
Title of host publication2009 International Conference on Signal Processing Systems, ICSPS 2009
Pages984-988
Number of pages5
DOIs
Publication statusPublished - 2009
Event2009 International Conference on Signal Processing Systems, ICSPS 2009 - Singapore
Duration: 15 May 200917 May 2009

Other

Other2009 International Conference on Signal Processing Systems, ICSPS 2009
CitySingapore
Period15/5/0917/5/09

Fingerprint

Time series
Fatigue of materials
Decomposition
Railroad cars
Decision making

Keywords

  • Additive
  • Component
  • Cyclical
  • Fatigue
  • Irregular
  • Seasonal
  • Time series behaviour
  • Trend

ASJC Scopus subject areas

  • Signal Processing

Cite this

Mohd Nopiah, Z., Baharin, M. N., Abdullah, S., & Khairir, M. I. (2009). Time series behaviour of lower arm suspension fatigue data using classical decomposition method. In 2009 International Conference on Signal Processing Systems, ICSPS 2009 (pp. 984-988). [5166939] https://doi.org/10.1109/ICSPS.2009.180

Time series behaviour of lower arm suspension fatigue data using classical decomposition method. / Mohd Nopiah, Zulkifli; Baharin, M. N.; Abdullah, Shahrum; Khairir, M. I.

2009 International Conference on Signal Processing Systems, ICSPS 2009. 2009. p. 984-988 5166939.

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

Mohd Nopiah, Z, Baharin, MN, Abdullah, S & Khairir, MI 2009, Time series behaviour of lower arm suspension fatigue data using classical decomposition method. in 2009 International Conference on Signal Processing Systems, ICSPS 2009., 5166939, pp. 984-988, 2009 International Conference on Signal Processing Systems, ICSPS 2009, Singapore, 15/5/09. https://doi.org/10.1109/ICSPS.2009.180
Mohd Nopiah Z, Baharin MN, Abdullah S, Khairir MI. Time series behaviour of lower arm suspension fatigue data using classical decomposition method. In 2009 International Conference on Signal Processing Systems, ICSPS 2009. 2009. p. 984-988. 5166939 https://doi.org/10.1109/ICSPS.2009.180
Mohd Nopiah, Zulkifli ; Baharin, M. N. ; Abdullah, Shahrum ; Khairir, M. I. / Time series behaviour of lower arm suspension fatigue data using classical decomposition method. 2009 International Conference on Signal Processing Systems, ICSPS 2009. 2009. pp. 984-988
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