全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

融合加速试验及外场使用信息的寿命评估方法

, PP. 947-951

Full-Text   Cite this paper   Add to My Lib

Abstract:

加速试验技术是开展寿命评估的重要手段,它能够在短时间内得到大量的寿命信息,弥补了外场信息稀缺的问题.然而实验室环境并不能完全代表外场使用环境,它们之间存在一定的差异,其结果也往往不能反应产品的实际情况.针对上述问题,提出一种能够综合加速寿命试验、加速退化试验和外场信息的贝叶斯建模评估方法,利用修正因子对实验室和外场的差异进行修正,利用马尔科夫蒙特卡洛方法进行统计推断,从而得到更为精确的外场可靠寿命及可靠性评估结果.最后通过仿真案例对该方法的实施过程进行了说明及验证,并对其精度和敏感性进行了分析.

References

[1]  Chhikara R S,Folks J L.The inverse Gaussian distribution as a lifetime model[J].Technometrics,1977,19(4):461-468
[2]  Whitmore G A,Schenkelberg F.Modelling accelerated degradation data using Wiener diffusion with a time scale transformation[J].Lifetime Data Analysis,1997,3(1):27-45
[3]  Anduin E Touw.Bayesian estimation of mixed Weibull distributions[J].Reliability Engineering & System Safety,2009,94:463-473
[4]  Chhikara R S,Folks J L.The inverse Gaussian distribution as a lifetime model[J].Technometrics,1977,19(4):461-468
[5]  Whitmore G A,Schenkelberg F.Modelling accelerated degradation data using Wiener diffusion with a time scale transformation[J].Lifetime Data Analysis,1997,3(1):27-45
[6]  Lu J.Degradation processes and related reliability models .Montreal:McGill University,1995
[7]  Lu J.Degradation processes and related reliability models .Montreal:McGill University,1995
[8]  Ioannis Ntzoufras.Bayesian modeling using WinBUGS[M].New York:John Wiely and Sons,2009;275-279
[9]  Gelman A,Rubin D B.Inference from iterative simulation using multiple sequences[J].Statistical Science,1992,7:457-472
[10]  Ioannis Ntzoufras.Bayesian modeling using WinBUGS[M].New York:John Wiely and Sons,2009;275-279
[11]  Gelman A,Rubin D B.Inference from iterative simulation using multiple sequences[J].Statistical Science,1992,7:457-472
[12]  Meeker William Q,Escobar Luls A,Hong Yili.Using accelerated life tests results to predict product field reliability [J].Technometrics,2009,51(2):146-161
[13]  Liao Haitao,Elsayed Elsayed A.Reliability inference for field conditions from accelerated degradation testing[J].Naval Research Logistics (NRL),2006,53(6):576-587
[14]  Pan Rong.A Bayes approach to reliability prediction utilizing data from accelerated life tests and field failure observations[J].Quality and Reliability Engineering International,2009,25(2):229-240
[15]  Meeker William Q,Escobar Luls A,Hong Yili.Using accelerated life tests results to predict product field reliability [J].Technometrics,2009,51(2):146-161
[16]  Hamada M,Martz H F,Reese C S,et al.A fully Bayesian approach for combining multilevel failure information in fault tree quantification and optimal follow-on resource allocation[J].Reliability Engineering & System Safety,2004,86:297-305
[17]  Pan Rong.A Bayes approach to reliability prediction utilizing data from accelerated life tests and field failure observations[J].Quality and Reliability Engineering International,2009,25(2):229-240
[18]  Graves T L,Hamada M S,Klamann R,et al.A fully Bayesian approach for combining multi-level information in multi-state fault tree quantification[J].Reliability Engineering & System Safety,2007,92:1476-1483
[19]  Hamada M,Martz H F,Reese C S,et al.A fully Bayesian approach for combining multilevel failure information in fault tree quantification and optimal follow-on resource allocation[J].Reliability Engineering & System Safety,2004,86:297-305
[20]  Graves T L,Hamada M S,Klamann R,et al.A fully Bayesian approach for combining multi-level information in multi-state fault tree quantification[J].Reliability Engineering & System Safety,2007,92:1476-1483
[21]  Graves T L,Hamada M S,Klamann R,et al.Using simultaneous higher-level and partial lower-level data in reliability assessments[J].Reliability Engineering & System Safety,2008,93:1273-1279
[22]  Graves T L,Hamada M S,Klamann R,et al.Using simultaneous higher-level and partial lower-level data in reliability assessments[J].Reliability Engineering & System Safety,2008,93:1273-1279
[23]  Anduin E Touw.Bayesian estimation of mixed Weibull distributions[J].Reliability Engineering & System Safety,2009,94:463-473

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133