A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools(NCMTs) reveal zero failures after a reliability test. Thus...A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools(NCMTs) reveal zero failures after a reliability test. Thus, the zero-failure data form and corresponding Bayesian model are developed to solve the zero-failure problem of NCMTs, for which no previous suitable statistical model has been developed. An expert-judgment process that incorporates prior information is presented to solve the difficulty in obtaining reliable prior distributions of Weibull parameters. The equations for the posterior distribution of the parameter vector and the Markov chain Monte Carlo(MCMC) algorithm are derived to solve the difficulty of calculating high-dimensional integration and to obtain parameter estimators. The proposed method is applied to a real case; a corresponding programming code and trick are developed to implement an MCMC simulation in Win BUGS, and a mean time between failures(MTBF) of 1057.9 h is obtained. Given its ability to combine expert judgment, prior information, and data, the proposed reliability modeling and assessment method under the zero failure of NCMTs is validated.展开更多
现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MC...现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)的稳控系统动态可靠性评估方法。首先针对失效过程,构建四状态非齐次马尔可夫模型来模拟装置老化过程,并给出各状态评判方法;其次针对修复过程,分析不同检修策略对装置状态转移的影响以体现状态检修的差异性;最后考虑稳控装置状态转移过程的时序或条件相关性,对稳控系统可靠性进行动态建模。以实际稳控系统为例,仿真对比不同检修策略下的可靠性,并对模型参数进行灵敏度分析。评估结果表明,该方法可以求解稳控系统的时变可用度,用于指导稳控装置现场合理检修。展开更多
基金Project(2014ZX04014-011)supported by State Key Science&Technology Program of ChinaProject([2016]414)supported by the 13th Five-year Program of Education Department of Jilin Province,China
文摘A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools(NCMTs) reveal zero failures after a reliability test. Thus, the zero-failure data form and corresponding Bayesian model are developed to solve the zero-failure problem of NCMTs, for which no previous suitable statistical model has been developed. An expert-judgment process that incorporates prior information is presented to solve the difficulty in obtaining reliable prior distributions of Weibull parameters. The equations for the posterior distribution of the parameter vector and the Markov chain Monte Carlo(MCMC) algorithm are derived to solve the difficulty of calculating high-dimensional integration and to obtain parameter estimators. The proposed method is applied to a real case; a corresponding programming code and trick are developed to implement an MCMC simulation in Win BUGS, and a mean time between failures(MTBF) of 1057.9 h is obtained. Given its ability to combine expert judgment, prior information, and data, the proposed reliability modeling and assessment method under the zero failure of NCMTs is validated.
文摘现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)的稳控系统动态可靠性评估方法。首先针对失效过程,构建四状态非齐次马尔可夫模型来模拟装置老化过程,并给出各状态评判方法;其次针对修复过程,分析不同检修策略对装置状态转移的影响以体现状态检修的差异性;最后考虑稳控装置状态转移过程的时序或条件相关性,对稳控系统可靠性进行动态建模。以实际稳控系统为例,仿真对比不同检修策略下的可靠性,并对模型参数进行灵敏度分析。评估结果表明,该方法可以求解稳控系统的时变可用度,用于指导稳控装置现场合理检修。
