Distribution characterisation of coil spring strain histories using mixed weibull analysis

Research output: Contribution to journalArticle

Abstract

This paper investigates the scatter of strain histories obtained from coil spring of a vehicle suspension system. Statistical characterisation is essential in fatigue analysis due to the random nature of fatigue process. The element of uncertainty can be dealt with appropriately by applying probabilistic approach. In this study, four different road profiles were used to obtain strain signal data. Initial description of the strain histories was achieved by computing the global statistics values. The strain range data was calculated from the counted fatigue cycle and distribution fitting was performed using the Anderson Darling test. Four different types of distribution; the exponent, Gamma, 3P-Weibull, and 2P-Weibull; were compared to find the appropriate fit to model the strain range data. The results showed that strain range data are highly skewed with thick-tailed indicating a non-Gaussian distribution. All four tested distribution turned out to be insignificance, with the closest p-value of 0.01 produced by the 2P-Weibull distribution at significance level of 0.05. Due to the non-straight probability plot of data, the mixed Weibull distribution was chosen to model the data. The campus area and highway profiles follow this distribution with 2 sub-populations while rural and housing area resulted in 3 sub-populations. Finally, this distribution is found suitable for analysing the strain range data of vehicle coil spring and hence can be used in time-domain fatigue life evaluation.

Original languageEnglish
Pages (from-to)110-117
Number of pages8
JournalInternational Journal of Engineering and Technology(UAE)
Volume7
Issue number3
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

Fatigue
Fatigue of materials
Weibull distribution
Rural Population
Uncertainty
Suspensions
Vehicle suspensions
Population
Statistics

Keywords

  • Coil spring
  • Distribution
  • Mixed weibull
  • Strain signal
  • Suspension

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science (miscellaneous)
  • Environmental Engineering
  • Chemical Engineering(all)
  • Engineering(all)
  • Hardware and Architecture

Cite this

@article{d1d51fad20264ea9b30a843de6d32b92,
title = "Distribution characterisation of coil spring strain histories using mixed weibull analysis",
abstract = "This paper investigates the scatter of strain histories obtained from coil spring of a vehicle suspension system. Statistical characterisation is essential in fatigue analysis due to the random nature of fatigue process. The element of uncertainty can be dealt with appropriately by applying probabilistic approach. In this study, four different road profiles were used to obtain strain signal data. Initial description of the strain histories was achieved by computing the global statistics values. The strain range data was calculated from the counted fatigue cycle and distribution fitting was performed using the Anderson Darling test. Four different types of distribution; the exponent, Gamma, 3P-Weibull, and 2P-Weibull; were compared to find the appropriate fit to model the strain range data. The results showed that strain range data are highly skewed with thick-tailed indicating a non-Gaussian distribution. All four tested distribution turned out to be insignificance, with the closest p-value of 0.01 produced by the 2P-Weibull distribution at significance level of 0.05. Due to the non-straight probability plot of data, the mixed Weibull distribution was chosen to model the data. The campus area and highway profiles follow this distribution with 2 sub-populations while rural and housing area resulted in 3 sub-populations. Finally, this distribution is found suitable for analysing the strain range data of vehicle coil spring and hence can be used in time-domain fatigue life evaluation.",
keywords = "Coil spring, Distribution, Mixed weibull, Strain signal, Suspension",
author = "M. Mahmud and Shahrum Abdullah and Singh, {S. S.K.} and {Mohd Ihsan}, {Ahmad Kamal Ariffin} and {Mohd Nopiah}, Zulkifli and Azli Arifin",
year = "2018",
month = "1",
day = "1",
doi = "10.14419/ijet.v7i3.17.16632",
language = "English",
volume = "7",
pages = "110--117",
journal = "International Journal of Engineering and Technology(UAE)",
issn = "2227-524X",
publisher = "Science Publishing Corporation Inc",
number = "3",

}

TY - JOUR

T1 - Distribution characterisation of coil spring strain histories using mixed weibull analysis

AU - Mahmud, M.

AU - Abdullah, Shahrum

AU - Singh, S. S.K.

AU - Mohd Ihsan, Ahmad Kamal Ariffin

AU - Mohd Nopiah, Zulkifli

AU - Arifin, Azli

PY - 2018/1/1

Y1 - 2018/1/1

N2 - This paper investigates the scatter of strain histories obtained from coil spring of a vehicle suspension system. Statistical characterisation is essential in fatigue analysis due to the random nature of fatigue process. The element of uncertainty can be dealt with appropriately by applying probabilistic approach. In this study, four different road profiles were used to obtain strain signal data. Initial description of the strain histories was achieved by computing the global statistics values. The strain range data was calculated from the counted fatigue cycle and distribution fitting was performed using the Anderson Darling test. Four different types of distribution; the exponent, Gamma, 3P-Weibull, and 2P-Weibull; were compared to find the appropriate fit to model the strain range data. The results showed that strain range data are highly skewed with thick-tailed indicating a non-Gaussian distribution. All four tested distribution turned out to be insignificance, with the closest p-value of 0.01 produced by the 2P-Weibull distribution at significance level of 0.05. Due to the non-straight probability plot of data, the mixed Weibull distribution was chosen to model the data. The campus area and highway profiles follow this distribution with 2 sub-populations while rural and housing area resulted in 3 sub-populations. Finally, this distribution is found suitable for analysing the strain range data of vehicle coil spring and hence can be used in time-domain fatigue life evaluation.

AB - This paper investigates the scatter of strain histories obtained from coil spring of a vehicle suspension system. Statistical characterisation is essential in fatigue analysis due to the random nature of fatigue process. The element of uncertainty can be dealt with appropriately by applying probabilistic approach. In this study, four different road profiles were used to obtain strain signal data. Initial description of the strain histories was achieved by computing the global statistics values. The strain range data was calculated from the counted fatigue cycle and distribution fitting was performed using the Anderson Darling test. Four different types of distribution; the exponent, Gamma, 3P-Weibull, and 2P-Weibull; were compared to find the appropriate fit to model the strain range data. The results showed that strain range data are highly skewed with thick-tailed indicating a non-Gaussian distribution. All four tested distribution turned out to be insignificance, with the closest p-value of 0.01 produced by the 2P-Weibull distribution at significance level of 0.05. Due to the non-straight probability plot of data, the mixed Weibull distribution was chosen to model the data. The campus area and highway profiles follow this distribution with 2 sub-populations while rural and housing area resulted in 3 sub-populations. Finally, this distribution is found suitable for analysing the strain range data of vehicle coil spring and hence can be used in time-domain fatigue life evaluation.

KW - Coil spring

KW - Distribution

KW - Mixed weibull

KW - Strain signal

KW - Suspension

UR - http://www.scopus.com/inward/record.url?scp=85051000208&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051000208&partnerID=8YFLogxK

U2 - 10.14419/ijet.v7i3.17.16632

DO - 10.14419/ijet.v7i3.17.16632

M3 - Article

VL - 7

SP - 110

EP - 117

JO - International Journal of Engineering and Technology(UAE)

JF - International Journal of Engineering and Technology(UAE)

SN - 2227-524X

IS - 3

ER -