Evaluating Confidence Interval of Fatigue Damage from One Single Measured Non-stationary Time-History

Marques, Julian Marcell Enzveiler; Benasciutti, Denis

doi:10.1007/978-3-030-97822-8_22

Cited by 2 publications

(8 citation statements)

References 3 publications

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“…When the switching time-history z(t) has only one stationary state (N S =1), the previous confidence interval expression converges to the solution given in [6] for the stationary case. Numerical and experimental results have confirmed the validity of the above confidence interval for both stationary and non-stationary random loadings [6,8,9].…”

Section: Confidence Intervals For the Expected Damage E[d(t)]supporting

confidence: 62%

“…Note that each state needs not to appear in z(t) only once in its full length T j , but it can appear a multitude of times, each with duration shorter than T j , provided that its total time length is T j . It is irrelevant in which time order and how many times a state appear in z(t), so at the beginning of the analysis, the states are reordered so that they appear only once in their full length T j [8,9].…”

Section: Confidence Intervals For the Expected Damage E[d(t)]mentioning

confidence: 99%

“…Damage D B,ij (T B ) refers to block i in state j. Under the assumption of D(T) normally distributed, the confidence the confidence interval for the expected damage E[D(T)] then is [8,9]:…”

Section: Confidence Intervals For the Expected Damage E[d(t)]mentioning

confidence: 99%

“…This aspect motivated our attempt to develop a different approach in which the statistical variability of fatigue damage is estimated by means of confidence intervals, constructed after a direct analysis of one or more time-histories [6]. First developed for the case of stationary random loadings, the approach has been extended to non-stationary loadings of "switching type" (i.e., formed by a sequence of stationary states) [8,9]. The method has been benchmarked, first against numerically simulated timehistories, and then against more realistic time-histories measured in a mountain-bike in off-road tracks; in either case, a quite good accuracy was observed [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…First developed for the case of stationary random loadings, the approach has been extended to non-stationary loadings of "switching type" (i.e., formed by a sequence of stationary states) [8,9]. The method has been benchmarked, first against numerically simulated timehistories, and then against more realistic time-histories measured in a mountain-bike in off-road tracks; in either case, a quite good accuracy was observed [8,9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The use of fractional order statistics for estimating nonparametric confidence intervals for quantiles of the fatigue damage computed in service random loadings

Benasciutti

Marques

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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This paper deals with the statistical variability of fatigue damage in random loadings as caused by the inherent randomness of the loading. It examines the use of a nonparametric method based on fractional order statistics as a tool for constructing confidence intervals for the quantile of fatigue damage, regardless of the knowledge of the damage probability distribution. The approach here discussed relies on a statistical method existing in the literature. After a short review of this method, with also an emphasis on possible advantages and limitations, the paper presents a numerical benchmark, followed by a practical application to time-histories measured on a mountain-bike in off-road tracks. The paper concludes with some critical remarks and an outlook.

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Section: Confidence Intervals For the Expected Damage E[d(t)]supporting

confidence: 62%

Section: Confidence Intervals For the Expected Damage E[d(t)]mentioning

confidence: 99%

“…Damage D B,ij (T B ) refers to block i in state j. Under the assumption of D(T) normally distributed, the confidence the confidence interval for the expected damage E[D(T)] then is [8,9]:…”

Section: Confidence Intervals For the Expected Damage E[d(t)]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The use of fractional order statistics for estimating nonparametric confidence intervals for quantiles of the fatigue damage computed in service random loadings

Benasciutti

Marques

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Fatigue Analysis of Nonstationary Random Loadings Measured in an Industrial Vehicle Wheel: Uncertainty of Fatigue Damage

Marques

Solazzi

Benasciutti

2022

Metals

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This article presents an application of a method for estimating the inherent statistical variability of the fatigue damage computed in one single nonstationary random time history. The method applies the concept of confidence interval for the damage, which is constructed after the single time history is subdivided into pseudo-stationary segments, with each of them further divided into shorter blocks. As a case study, the method is applied to the strain time histories measured in a wheel of a telescopic handler industrial vehicle. A preliminary screening involving the short-time Fourier transform and the run test is carried out to verify whether the measured time histories are truly nonstationary and fall within the hypotheses of the proposed method. After that, the confidence interval for the unknown expected damage is computed; its upper bound can be used as a safety limit in a structural integrity assessment. The obtained results seem very promising and suggest the possible use of the proposed approach in similar engineering applications.

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Evaluating Confidence Interval of Fatigue Damage from One Single Measured Non-stationary Time-History

Cited by 2 publications

References 3 publications

The use of fractional order statistics for estimating nonparametric confidence intervals for quantiles of the fatigue damage computed in service random loadings

The use of fractional order statistics for estimating nonparametric confidence intervals for quantiles of the fatigue damage computed in service random loadings

Fatigue Analysis of Nonstationary Random Loadings Measured in an Industrial Vehicle Wheel: Uncertainty of Fatigue Damage

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