Small-Sample Accelerated Life Test Method based on the Inverse Power Law Model

Wang, Yulin; Zhou, Bin; Tian, Ge; Feng, Hao; Tao, Weijun

doi:10.12785/amis/080429

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Generally, the mean values of the posterior distribution are used to estimate the distribution parameters. The unreliability of rubber sealing rings after storage t days is given by equation (44) F t ð Þ = 0:2 3 1 73:7 8:459…”

Section: Discussionmentioning

confidence: 99%

“…Normally, the relationship between the aging rate and accelerating factor can be described by failure physical models, such as Arrhenius model, 42 Eying model. 43 Furthermore, this relationship can also be described by empirical models, such as inverse power law model, 44,45 hazard regression model. 46 The Arrhenius equation is a formula for the temperature dependence of reaction rates.…”

Section: Preprocessing Of the Adt Degradation Datamentioning

confidence: 99%

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Bayesian model averaging based storage lifetime assessment method for rubber sealing rings

Liu

Wang

Zhang

et al. 2019

Advances in Mechanical Engineering

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Rubber sealing ring is one of the most widely used seals. It is always stored for a period of time before put into use, especially in aeronautic and aerospace applications. It is necessary to evaluate the storage lifetime of rubber sealing rings. However, due to the long storage lifetime of rubber sealing rings, two issues need to be handled, including model uncertainty and lack of storage lifetime data. A Bayesian model averaging based storage lifetime assessment method for rubber sealing rings is proposed in this article. The Gamma distribution model and Weibull distribution model are selected as the candidate models and combined based on Bayesian model averaging method. The Bayesian model averaging method is applied to handle the model uncertainty. Considering the lack of storage lifetime data, the degradation data are utilized to give the priors of model probability and distribution parameters based on the similarity principle. The results indicate that the proposed method has smaller minus log-likelihood value and is better than the other discussed method, considering both goodness of fit and complexity.

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Section: Discussionmentioning

confidence: 99%

Section: Preprocessing Of the Adt Degradation Datamentioning

confidence: 99%

Bayesian model averaging based storage lifetime assessment method for rubber sealing rings

Liu

Wang

Zhang

et al. 2019

Advances in Mechanical Engineering

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“…Although accelerated life testing can help estimate product or component reliability, 3,4 these results do not capture accurately the actual product performance under the real usage environment. On the other hand, field failure data provide more reliable information about distribution compared to laboratory data 5–7 since field data capture actual usage profile and the combined environmental exposure that are difficult to simulate in laboratory.…”

Section: Introductionmentioning

confidence: 99%

Reliability estimation considering usage rate profile and warranty claims

Limon

Yadav

Zuo

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part O:

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Providing more realistic reliability prediction based on small proportion of failed population or test data has always been a challenging task. Manufacturers rely heavily on reliability prediction for designing warranty plan. Furthermore, to predict warranty claims for the remaining warranty period, it is important to have more realistic reliability assessment by considering a larger proportion of the population or the maximum possible information on the remaining population. However, generally this information is not readily available and is very difficult to gather on the scattered population. In this work, we propose to use customer usage rate profile to generate censored usage data on the remaining population that do not have any failure and warranty claims yet. We intend to use field data available such as warranty claims, field failures, recall data, and maintenance data to develop usage rate profile and subsequently estimate censored usage time. Finally, reliability estimation methodology is developed considering both censored data and field failure data to provide more reasonable reliability prediction for the remaining warranty period. The proposed methodology is demonstrated considering real-life data from a big manufacturing company.

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A bivariate dependent degradation model based on artificial neural network supported stochastic process and Copula function

Liu,

Duan,

Wang

et al. 2024

Quality & Reliability Eng

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In order to use the high ability of the artificial neural network (ANN) in data fitting, this paper introduces an ANN in stochastic process to describe the mean function for degradation modeling. Due to the fact that the existing method cannot handle the bivariate dependent degradation conditions, a bivariate dependent degradation model based on Copula function and ANN‐supported stochastic processes is proposed. Considering the random effects caused by individual difference, it is assumed that the unknown parameters in the stochastic processes and Copula functions are randomly distributed. Based on the maximum likelihood and moment estimation methods, a related statistical inference method for ANN training and parameter estimation is developed to use the bivariate dependent degradation model. An actual fatigue crack dataset is used to demonstrate the validity of the proposed method. The obtained results show that the dependent relationship between two degradation indicators should not be neglected, and it can be efficiently handled by the proposed method. Furthermore, the proposed degradation model can provide reliability and degradation intervals with enough precision due to the fact that it considers the random effects caused by individual difference.

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Small-Sample Accelerated Life Test Method based on the Inverse Power Law Model

Cited by 7 publications

References 11 publications

Bayesian model averaging based storage lifetime assessment method for rubber sealing rings

Bayesian model averaging based storage lifetime assessment method for rubber sealing rings

Reliability estimation considering usage rate profile and warranty claims

A bivariate dependent degradation model based on artificial neural network supported stochastic process and Copula function

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