Bootstrap analysis of designed experiments for reliability improvement with a non-constant scale parameter

Wang, Guodong; He, Zhen; Li, Xue; Qing-an, Cui; Lv, Shanshan; Panpan, Zhou

doi:10.1016/j.ress.2016.12.006

Cited by 22 publications

(40 citation statements)

References 24 publications

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“…The bootstrap technique is mostly used to infer statistical random data based on simulations. The main goal of this method is to resample random data from an original set that produced a large number of repeated random datasets to approximate the statistics of the sampling distribution, as depicted in Figure 7 [43]. In this research, life data were considered as sample size (n = 9) based on the strain approach from rural, campus, and highway road excitations.…”

Section: Proposed Probabilistic Methods Based On the Gumbel Modelmentioning

confidence: 99%

Fatigue Reliability Assessment of an Automobile Coil Spring under Random Strain Loads Using Probabilistic Technique

Manouchehrynia

Abdullah

Singh

2019

Metals

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This paper presents a mathematical model to estimate strain-life probabilistic modeling based on the fatigue reliability prediction of an automobile coil spring under random strain loads. The proposed technique was determined using a probabilistic method of the Gumbel distribution for strain-life models of automobile suspension systems. Strain signals from different road excitations in experimental tests were measured. The probability density function of the Gumbel distribution was considered to estimate model parameters using maximum likelihood estimation (MLE). The Akaike information criterion (AIC) method was performed to specify which model can estimate the best fit model parameters. Results demonstrated a good agreement between the predicted fatigue lives of the proposed probabilistic model and the measured strain fatigue life models. The root-mean-square errors (RMSE) based on the Coffin–Manson, Morrow, and Smith–Watson–Topper strain-life models were approximately 0.00114, 0.00107, and 0.00509, respectively, indicating a high correlation with the proposed model and experimental data. The results demonstrated that the proposed probabilistic model is effective for the fatigue life prediction of automobile coil springs using strain and stress fatigue life approaches.

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Section: Proposed Probabilistic Methods Based On the Gumbel Modelmentioning

confidence: 99%

Fatigue Reliability Assessment of an Automobile Coil Spring under Random Strain Loads Using Probabilistic Technique

Manouchehrynia

Abdullah

Singh

2019

Metals

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“…cending order. To deal with the bias induced by the parametric bootstrapping methods, we use the bias-corrected and accelerated (BCa) bootstrap to construct confidence intervals for [33], [34]. Specifically, let be the th element of , and sort the sample for each parameter as �̂ * Further,…”

Section: Order the Estimates Of For Each Parameter In As-mentioning

confidence: 99%

Reliability Modeling and Analysis of Load-Sharing Systems With Continuously Degrading Components

Zhao

Liu

2018

IEEE Trans. Rel.

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This paper presents a reliability modeling and analysis framework for load-sharing systems with identical components subject to continuous degradation. It is assumed that the components in the system suffer from degradation through an additive impact under increased workload caused by consecutive failures. A log-linear link function is used to describe the relationship between the degradation rate and load stress levels. By assuming that the component degradation is well modeled by a step-wise drifted Wiener process, we construct maximum likelihood estimates (MLEs) for unknown parameters and related reliability characteristics by combining analytical and numerical methods. Approximate initial guesses are proposed to lessen the computational burden in numerical estimation. The estimated distribution of MLE is given in the form of multivariate normal distribution with the aid of Fisher information. Alternative confidence intervals are provided by bootstrapping methods. A simulation study with various sample sizes and inspection intervals is presented to analyze the estimation accuracy. Finally, the proposed approach is illustrated by track degradation data from an application example.

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“…From this point of perspective, in order to improve the inferential accuracy of extrapolation, various studies of ALTs for real life data are discussed by many authors, where some general life-stress relationships are assumed with multiple stress-affected parameters in probability model that show superior performance than classical sole stress-affected parameter assumption. See the works of Lv et al, 6 Si and Yang, 7 Wang et al, 8,9 as well as references therein.…”

Section: Introduction and Notationmentioning

confidence: 99%

Reliability analysis of Weibull multicomponent system with stress‐dependent parameters from accelerated life data

Wang

Zhang

Tripathi

et al. 2021

Quality & Reliability Eng

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In this paper, reliability estimation of multicomponent system under a multilevel accelerated life testing. When the lifetime of components follows Weibull distribution, the problem of point and interval estimates are discussed from different perspectives. Under a general life-stress assumption that there are multiple nonconstant and stress-dependent scale and shape parameters, the maximum likelihood estimates of unknown parameters along with associated existence and uniqueness are established. Approximate confidence intervals are constructed correspondingly via expected Fisher information matrix. Furthermore, some pivotal quantities are constructed and alternative generalized point and interval estimates are also proposed for comparison. In addition, predictive intervals for the lifetime of the multicomponent system are discussed under classical and generalized pivotal approaches, respectively. The results show that the proposed generalized estimates are superior to the conventional likelihood approach in terms of the accuracy. A real data example is carried out to illustrate the implementations of the proposed methods.

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Bootstrap analysis of designed experiments for reliability improvement with a non-constant scale parameter

Cited by 22 publications

References 24 publications

Fatigue Reliability Assessment of an Automobile Coil Spring under Random Strain Loads Using Probabilistic Technique

Fatigue Reliability Assessment of an Automobile Coil Spring under Random Strain Loads Using Probabilistic Technique

Reliability Modeling and Analysis of Load-Sharing Systems With Continuously Degrading Components

Reliability analysis of Weibull multicomponent system with stress‐dependent parameters from accelerated life data

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