数控机床不完全维修的贝叶斯可靠性评估

摘要/Abstract

摘要：

提出了在少样本故障数据情况下，数控机床不完全维修的贝叶斯可靠性评估方法，分析了广义更新过程虚龄模型参数的验前分布和后验分布，利用马尔科夫链蒙特卡洛仿真方法获得了模型参数、累计故障数、故障强度和可靠度等可靠性指标的贝叶斯点估计和区间估计，并分析了某一现场数控机床不完全维修的可靠性.结果表明，与极大似然估计方法相比，贝叶斯可靠性评估方法具有较高的精度.

关键词: 不完全维修, 少样本, 故障数据, 贝叶斯可靠性, 数控机床

Abstract:

A Bayesian reliability assessment method of small sample failure data for numerically controlled machine tools with imperfect repair was proposed. The selection of prior and posterior distributions for virtual age model of generalized renewal process was discussed. Bayesian point and interval estimates of model parameters and reliability indices including cumulative failure number, failure intensity and reliability etc. were obtained via Markov chain Monte Carlo (MCMC) sampling. A real example was analyzed, whose results show that Bayesian reliability analysis method has a higher accuracy than maximum likelihood estimate because of its incorporating prior information.

Key words: imperfect repair, small sample, failure data, Bayesian reliability, numerically controlled machine tool

中图分类号:

王智明1,杨建国2. 数控机床不完全维修的贝叶斯可靠性评估[J]. 上海交通大学学报（自然版）, 2014, 48(05): 614-617.

WANG Zhiming1,YANG Jianguo2. Bayesian Reliability Assessment for Numerically Controlled Machine Tools with Imperfect Repair[J]. Journal of Shanghai Jiaotong University, 2014, 48(05): 614-617.

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