Reliability Evaluation of High-speed Train Bearing Based on Stochastic Performance Deterioration with Minimum Sample
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摘要: 针对高速列车轴承性能退化过程缓慢、退化轨迹较为平稳的特点,本文采用随机性能退化过程建模,将Wiener过程的未知参数看作随机变量,根据轴承相对温度性能退化幅度的大小以及工程经验确定出合理的退化轨迹模型,最终得出轴承寿命的可靠度函数。通过对某型号轴承试验数据的实例分析,表明文中的方法能够在极小样本无失效数据情况下充分利用性能退化数据,完成产品寿命的可靠性评估。Abstract: In the light of the features of high-speed train bearing such as the slow deterioration process and the smooth deterioration path,the stochastic performance deterioration process model is established through viewing the unknown parameters of the Wiener process as stochastic variables.According to the size of the bearing relative temperature(relative to the environmental temperature) performance deterioration amplitude and the engineering experiences,the reasonable deterioration path model is determined and the reliability function of bearing life is finally obtained.The feasibility of proposed method is proved and the bearing life reliability evaluation can be completed by analyzing a bearing testing data in the case of the minimum sample and zero-failure.Thus the method provides a guide for the high-speed train bearing reliability evaluation problem.
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[1] 朱德馨,刘宏昭. 极小样本下高速列车轴承的可靠性评估[J]. 中南大学学报,2013,44(3):963~969 [2] Wang X. Wiener processes with random effects for degradationdata[J]. Journal of Multivariate Analysis,2010,101(2):340~351 [3] 彭宝华,周经纶,金光. 综合多种信息的金属化膜电容器可靠性评估[J]. 强激光与粒子束,2009,21(8):1271~1275 [4] Tseng S T,Tang J,Ku I H. Determination of optimal burn-inparameters and residual life for highly reliable products[J].Naval Research Logistics,2003,50:1~14 [5] Tang J,Su T S. Estimating failure time distribution and its pa-rameters based on intermediate data from a Wiener degradationmodel[J]. Naval Research Logistics,2008,55(3):265~276 [6] Nicolai R P,Dekker R,Noortwijk J M. A comparison of modelsfor measurable deterioration: An application to coatings on steelstructures [J]. Reliability Engineering and System Safety,2007,92(12):1635~160 [7] Whitemore G A. Estimating degradation by a Wiener diffusionprocess subject to measurement error[J]. Lifetime Data Analy-sis,1995,1:307~319 [8] Whitemore G A,Schenkelberg F. Modelling accelerated degra-tion data using Wiener diffusion with a scale transformation[J].Lifetime Data Analysis,1997,3(1):27~45 [9] Lee M L T,Whitmore G A. Threshold regression for survival a-nalysis: modeling event times by a stochastic process reaching aboundary[J]. Statistical Science,2006,21(4):501~513 [10] Yuan X X. Stochastic Modeling of Deteriortaion in NuclearPower Plant Components[D]. Ontario,Canada: University ofWaterloo,2007 [11] Balka J,Desmond A F,McNicholas P D. Review and implemen-tation of cure models based on first hitting times for Wienerprocesses[J]. Lifetime Data Analysis,2009,15 (12):147~176 [12] Chikkara R S,Folks J L. The Inverse Gaussian Distribution [M].New York: Marcell Dekker,1989 [13] Efron B,Tibshirani R. An Introduction to the Bootstrap[M]. NewYork: Cha pman and Hall,1993 -

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