数控机床贝叶斯可靠性评估模型的综合评价方法

Comprehensive Evaluation Approach to Bayesian Reliability Assessment Model of NC Machine tools

基于DIC(Deviance Information Criterion)信息准则、BGR(Brooks-Gelman-Rubin)诊断原理、蒙特卡洛仿真误差及模型参数和可靠性指标后验估计的区间长度,提出了数控机床贝叶斯可靠性模型的综合评价方法.给出了不同先验下用于Gibbs抽样的幂律过程模型参数的后验分布,并利用马尔科夫链蒙特卡洛法获得了模型参数和可靠性指标的贝叶斯点估计和区间估计.通过2个工程实例进行验证,结果表明,幂律过程模型各项评价指标均优于Weibull分布模型,适用于小样本故障数据数控机床的可靠性评估.

Based on deviance information criterion, Brooks-Gelman-Rubin diagnosis principle, Monte Carlo simulation error, and the interval length of posterior estimate for model parameters and reliability indices, the comprehensive evaluation method for Bayesian reliability model of NC machine tools was proposed. The posterior distributions of power law process model used for Gibbs sampling at different priors were given, and the Bayesian point and interval estimate of model parameters and reliability indices were ob- tained by using Markov Chain Monte Carlo simulation. Two real engineering examples were taken to prove the proposed method. The results of each evaluation index show that the power law process model is better than the Weibull model, which is suitable for reliability assessment of NC machine tools with small sample failure data.