Probabilistic-based analysis for damaging features of fatigue strain loadings

This paper presents the behaviour of fatigue damage extraction in fatigue strain histories of automotive components using the probabilistic approach. This is a consideration for the evaluation of fatigue damage extraction in automotive components under service loading that is vital in a reliability...

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Main Authors: M. F. Mod Yunoh, S. Abdullah, M. H. M. Saad, Z. M. Nopiah, M. Z. Nuawi, S. S. K. Singh
Format: Article
Language:English
Published: Gruppo Italiano Frattura 2018-10-01
Series:Frattura ed Integrità Strutturale
Subjects:
Online Access:http://www.gruppofrattura.it/pdf/rivista/numero46/numero_46_art_9.pdf
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spelling doaj-04238d5bb5a4403ea4ebc2f1a3dcb95d2020-11-24T23:55:32ZengGruppo Italiano FratturaFrattura ed Integrità Strutturale1971-89932018-10-011246849310.3221/IGF-ESIS.46.0910.3221/IGF-ESIS.46.09Probabilistic-based analysis for damaging features of fatigue strain loadingsM. F. Mod YunohS. AbdullahM. H. M. SaadZ. M. NopiahM. Z. NuawiS. S. K. SinghThis paper presents the behaviour of fatigue damage extraction in fatigue strain histories of automotive components using the probabilistic approach. This is a consideration for the evaluation of fatigue damage extraction in automotive components under service loading that is vital in a reliability analysis. For the purpose of research work, two strain signals data are collected from a car coil spring during a road test. The fatigue strain signals are then extracted using the wavelet transform in order to extract the high amplitude segments that contribute to the fatigue damage. At this stage, the low amplitude segments are removed because of their minimal contribution to the fatigue damage. The fatigue damage based on all extracted segments is calculated using some significant strain-life models. Subsequently, the statistics-based Weibull distribution is applied to evaluate the fatigue damage extraction. It has been found that about 70% of the probability of failure occurs in the 1.0 x 10-5 to 1.0 x 10-4 damage range for both signals, while 90% of the probability of failure occurs in the 1.0 x 10-4 to 1.0 x 10-3 damage range. Lastly, it is suggested that the fatigue damage can be determined by the Weibull distribution analysishttp://www.gruppofrattura.it/pdf/rivista/numero46/numero_46_art_9.pdfFatigue damage Features extraction Probabilistic Wavelet Weibull distribution
collection DOAJ
language English
format Article
sources DOAJ
author M. F. Mod Yunoh
S. Abdullah
M. H. M. Saad
Z. M. Nopiah
M. Z. Nuawi
S. S. K. Singh
spellingShingle M. F. Mod Yunoh
S. Abdullah
M. H. M. Saad
Z. M. Nopiah
M. Z. Nuawi
S. S. K. Singh
Probabilistic-based analysis for damaging features of fatigue strain loadings
Frattura ed Integrità Strutturale
Fatigue damage
Features extraction
Probabilistic
Wavelet
Weibull distribution
author_facet M. F. Mod Yunoh
S. Abdullah
M. H. M. Saad
Z. M. Nopiah
M. Z. Nuawi
S. S. K. Singh
author_sort M. F. Mod Yunoh
title Probabilistic-based analysis for damaging features of fatigue strain loadings
title_short Probabilistic-based analysis for damaging features of fatigue strain loadings
title_full Probabilistic-based analysis for damaging features of fatigue strain loadings
title_fullStr Probabilistic-based analysis for damaging features of fatigue strain loadings
title_full_unstemmed Probabilistic-based analysis for damaging features of fatigue strain loadings
title_sort probabilistic-based analysis for damaging features of fatigue strain loadings
publisher Gruppo Italiano Frattura
series Frattura ed Integrità Strutturale
issn 1971-8993
publishDate 2018-10-01
description This paper presents the behaviour of fatigue damage extraction in fatigue strain histories of automotive components using the probabilistic approach. This is a consideration for the evaluation of fatigue damage extraction in automotive components under service loading that is vital in a reliability analysis. For the purpose of research work, two strain signals data are collected from a car coil spring during a road test. The fatigue strain signals are then extracted using the wavelet transform in order to extract the high amplitude segments that contribute to the fatigue damage. At this stage, the low amplitude segments are removed because of their minimal contribution to the fatigue damage. The fatigue damage based on all extracted segments is calculated using some significant strain-life models. Subsequently, the statistics-based Weibull distribution is applied to evaluate the fatigue damage extraction. It has been found that about 70% of the probability of failure occurs in the 1.0 x 10-5 to 1.0 x 10-4 damage range for both signals, while 90% of the probability of failure occurs in the 1.0 x 10-4 to 1.0 x 10-3 damage range. Lastly, it is suggested that the fatigue damage can be determined by the Weibull distribution analysis
topic Fatigue damage
Features extraction
Probabilistic
Wavelet
Weibull distribution
url http://www.gruppofrattura.it/pdf/rivista/numero46/numero_46_art_9.pdf
work_keys_str_mv AT mfmodyunoh probabilisticbasedanalysisfordamagingfeaturesoffatiguestrainloadings
AT sabdullah probabilisticbasedanalysisfordamagingfeaturesoffatiguestrainloadings
AT mhmsaad probabilisticbasedanalysisfordamagingfeaturesoffatiguestrainloadings
AT zmnopiah probabilisticbasedanalysisfordamagingfeaturesoffatiguestrainloadings
AT mznuawi probabilisticbasedanalysisfordamagingfeaturesoffatiguestrainloadings
AT ssksingh probabilisticbasedanalysisfordamagingfeaturesoffatiguestrainloadings
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