Effect of fatigue strain data behaviour using cycle counting method

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1 Citation (Scopus)

Abstract

This paper presents fatigue analysis on variable amplitude (VA) loading data using rainflow count and Markov count methods. The objective of this study is to observe the capability of this method in identifying the random behaviour and distribution in a fatigue time series data. For the purpose of this study, a set of case study data consist of nonstationary strain data that exhibits a random behaviour was used. This random data was collected in the unit of microstrain on the lower suspension arm of a car. The collected data was measured for 60 seconds at the sampling rate of 500 Hz, which gave 30,000 discrete data points. In order to compare the result from case study data, the strain signal was selected from the database of Society of Automotive Engineers (SAE) profiles of suspension component, i.e. the SAESUS. The distribution of strain data was then calculated and analysed in the form of rainflow count method, Markov count method and fatigue life assessment and they were then compared. The findings from this study are crucial in the determination of the signal's pattern behaviour existed in the VA signals.

Original languageEnglish
Pages (from-to)644-648
Number of pages5
JournalInternational Review of Mechanical Engineering
Volume6
Issue number3
Publication statusPublished - 2012

Fingerprint

Fatigue of materials
Suspensions (components)
Time series
Railroad cars
Sampling
Engineers

Keywords

  • Fatigue
  • Fatigue life
  • Markov count
  • Rainflow count
  • Variable amplitude

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

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title = "Effect of fatigue strain data behaviour using cycle counting method",
abstract = "This paper presents fatigue analysis on variable amplitude (VA) loading data using rainflow count and Markov count methods. The objective of this study is to observe the capability of this method in identifying the random behaviour and distribution in a fatigue time series data. For the purpose of this study, a set of case study data consist of nonstationary strain data that exhibits a random behaviour was used. This random data was collected in the unit of microstrain on the lower suspension arm of a car. The collected data was measured for 60 seconds at the sampling rate of 500 Hz, which gave 30,000 discrete data points. In order to compare the result from case study data, the strain signal was selected from the database of Society of Automotive Engineers (SAE) profiles of suspension component, i.e. the SAESUS. The distribution of strain data was then calculated and analysed in the form of rainflow count method, Markov count method and fatigue life assessment and they were then compared. The findings from this study are crucial in the determination of the signal's pattern behaviour existed in the VA signals.",
keywords = "Fatigue, Fatigue life, Markov count, Rainflow count, Variable amplitude",
author = "{Mohd Nopiah}, Zulkifli and A. Lennie and Shahrum Abdullah and Nuawi, {Mohd. Zaki} and {Ahmat Zainuri}, Nuryazmin and Baharin, {M. N.}",
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AU - Mohd Nopiah, Zulkifli

AU - Lennie, A.

AU - Abdullah, Shahrum

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AU - Ahmat Zainuri, Nuryazmin

AU - Baharin, M. N.

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N2 - This paper presents fatigue analysis on variable amplitude (VA) loading data using rainflow count and Markov count methods. The objective of this study is to observe the capability of this method in identifying the random behaviour and distribution in a fatigue time series data. For the purpose of this study, a set of case study data consist of nonstationary strain data that exhibits a random behaviour was used. This random data was collected in the unit of microstrain on the lower suspension arm of a car. The collected data was measured for 60 seconds at the sampling rate of 500 Hz, which gave 30,000 discrete data points. In order to compare the result from case study data, the strain signal was selected from the database of Society of Automotive Engineers (SAE) profiles of suspension component, i.e. the SAESUS. The distribution of strain data was then calculated and analysed in the form of rainflow count method, Markov count method and fatigue life assessment and they were then compared. The findings from this study are crucial in the determination of the signal's pattern behaviour existed in the VA signals.

AB - This paper presents fatigue analysis on variable amplitude (VA) loading data using rainflow count and Markov count methods. The objective of this study is to observe the capability of this method in identifying the random behaviour and distribution in a fatigue time series data. For the purpose of this study, a set of case study data consist of nonstationary strain data that exhibits a random behaviour was used. This random data was collected in the unit of microstrain on the lower suspension arm of a car. The collected data was measured for 60 seconds at the sampling rate of 500 Hz, which gave 30,000 discrete data points. In order to compare the result from case study data, the strain signal was selected from the database of Society of Automotive Engineers (SAE) profiles of suspension component, i.e. the SAESUS. The distribution of strain data was then calculated and analysed in the form of rainflow count method, Markov count method and fatigue life assessment and they were then compared. The findings from this study are crucial in the determination of the signal's pattern behaviour existed in the VA signals.

KW - Fatigue

KW - Fatigue life

KW - Markov count

KW - Rainflow count

KW - Variable amplitude

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