Fatigue features extraction of road load time data using the S-transform

Abdullah, Shahrum; Nizwan, C. K. E.; Yunoh, Mohd Faridz Mod; Nuawi, Mohd. Zaki; Nopiah, Zulkifli Mohd

doi:10.1007/s12239-013-0089-9

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…The distribution model is shown in Table 8. The Euler beta function B(a, b) and complementary error function erfc(x) in the PDF of Student's t-distribution and skew-normal distribution are shown as equations (22) and 23B a, b ð Þ=…”

Section: Fundingmentioning

confidence: 99%

“…In order to preferably reproduce the failure mode and accurately predict the failure time of the cylindrical shock absorber used in the area, a compilation method of multi-axial loading spectrum was proposed formed on the road spectrum. 21 Abdullah et al 22 designed the algorithm development of a new fatigue data editing technique using S-T approach. The algorithm can be used to extract the fatigue damaging events, the combination of which produces a new edited signal that neglects the low-amplitude cycles.…”

Section: Introductionmentioning

confidence: 99%

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Load spectrum for automotive wheels hub based on mixed probability distribution model

Geng

Liu

Yang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In actual engineering, the actual road test or indoor bench test is usually used to collect the data of the road load of the parts to acquire the fatigue life estimation of the auto parts. This paper proposes a method for load spectrum construction based on the mixed distribution probability model using the data of the road load spectrum collected in the test site. Pau Ta criteria outlier elimination and wavelet signal denoising are applied to analyze the original road load spectrum data. Then the maximum likelihood estimation method is used to estimate the generalized Pareto distribution parameters of all excesses. The Pareto distribution is also employed to extrapolate the load spectrum. Through the characteristic analysis of the load spectrum, the one-dimensional and two-dimensional program load spectrum of the hub is established based on the mixed probability distribution model, which provides a theoretical basis for the life prediction of the hub. In addition, the research results of this paper provide inspirations for the fatigue life prediction and fatigue durability bench test of automotive parts subjected to the complexity and variability of random loads.

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Section: Fundingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Load spectrum for automotive wheels hub based on mixed probability distribution model

Geng

Liu

Yang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Therefore, in many cases, the fatigue loading history is edited by removing these small amplitude cycles to produce representative and meaningful, yet economical testing, Stephens et al [2]. Several fatigue data editing techniques have been developed for use in the time domain analysis, Abdullah et al [3]. Some previous algorithms have been developed for eliminating low amplitude cycles in order to only retain the high amplitude cycles, El Ratal et al [4].…”

Section: Introductionmentioning

confidence: 99%

“…The filtering method does not shorten the signal because it does not provide the time-based information, Nizwan et al [5]. In addition, Abdullah et al [3] have developed a method for data editing to shorten the strain signal in the time-frequency domain. In fatigue data editing, the behaviour of extraction segments also needs to be studied because it contributes many bits of information that can improve fatigue life prediction.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic-Based Analysis for Damaging Features of Fatigue Strain Loadings

Yunoh¹,

Abdullah

Saad

et al. 2018

Frattura Ed Integrità Strutturale

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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 KEYWORDS.

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“…According to their study, different road surfaces will result in different levels of fatigue damage to the components. Therefore, several important analyses, such as the Short-Time Fourier Transform (STFT), the Wavelet Transform and the S-Transform, have been created in the time-frequency domain to detect fatigue damage caused by road factors [6,7].…”

Section: Introductionmentioning

confidence: 99%