Although Markov chain Monte Carlo（MCMC） algorithms are accurate, many factors may cause instability when they are utilized in reliability analysis; such instability makes these algorithms unsuitable for widespread e...Although Markov chain Monte Carlo（MCMC） algorithms are accurate, many factors may cause instability when they are utilized in reliability analysis; such instability makes these algorithms unsuitable for widespread engineering applications. Thus, a reliability modeling and assessment solution aimed at small-sample data of numerical control（NC） machine tools is proposed on the basis of Bayes theories. An expert-judgment process of fusing multi-source prior information is developed to obtain the Weibull parameters＇ prior distributions and reduce the subjective bias of usual expert-judgment methods. The grid approximation method is applied to two-parameter Weibull distribution to derive the formulas for the parameters＇ posterior distributions and solve the calculation difficulty of high-dimensional integration. The method is then applied to the real data of a type of NC machine tool to implement a reliability assessment and obtain the mean time between failures（MTBF）. The relative error of the proposed method is 5.8020×10-4 compared with the MTBF obtained by the MCMC algorithm. This result indicates that the proposed method is as accurate as MCMC. The newly developed solution for reliability modeling and assessment of NC machine tools under small-sample data is easy, practical, and highly suitable for widespread application in the engineering field; in addition, the solution does not reduce accuracy.展开更多
试运行期间平均故障间隔时间(mean time between failures,MTBF)是反映风电机组可靠性的重要指标,但由于此期间的运行故障数据样本少且故障停机随机性较强,现有MTBF分析方法的误差较大。针对此种小样本估计问题和故障的随机性,提出了一...试运行期间平均故障间隔时间(mean time between failures,MTBF)是反映风电机组可靠性的重要指标,但由于此期间的运行故障数据样本少且故障停机随机性较强,现有MTBF分析方法的误差较大。针对此种小样本估计问题和故障的随机性,提出了一种利用多台机组运行信息的MTBF估计方法。其基本思路是:根据风电机组安装及其故障数据的特点,构造具有相同配置的多台故障停机的随机截尾数据,对机组的可靠度进行Kaplan-Meier非参数估计;基于这种初步估计结果,再进行二参数威布尔(Weibull)分布拟合,并根据Weibull分布的性质计算得到机组的MTBF。该文利用北方某风电场的试运行数据,对2012年11月投产的20台风电机组进行了MTBF分析计算,结果表明该方法能够有效提高机组试运行期MTBF估计的精度。展开更多
基金Supported by Research on Reliability Assessment and Test Methods of Heavy Machine Tools,China(State Key Science&Technology Project High-grade NC Machine Tools and Basic Manufacturing Equipment,Grant No.2014ZX04014-011)Reliability Modeling of Machining Centers Considering the Cutting Loads,China(Science&Technology Development Plan for Jilin Province,Grant No.3D513S292414)Graduate Innovation Fund of Jilin University,China(Grant No.2014053)
文摘Although Markov chain Monte Carlo（MCMC） algorithms are accurate, many factors may cause instability when they are utilized in reliability analysis; such instability makes these algorithms unsuitable for widespread engineering applications. Thus, a reliability modeling and assessment solution aimed at small-sample data of numerical control（NC） machine tools is proposed on the basis of Bayes theories. An expert-judgment process of fusing multi-source prior information is developed to obtain the Weibull parameters＇ prior distributions and reduce the subjective bias of usual expert-judgment methods. The grid approximation method is applied to two-parameter Weibull distribution to derive the formulas for the parameters＇ posterior distributions and solve the calculation difficulty of high-dimensional integration. The method is then applied to the real data of a type of NC machine tool to implement a reliability assessment and obtain the mean time between failures（MTBF）. The relative error of the proposed method is 5.8020×10-4 compared with the MTBF obtained by the MCMC algorithm. This result indicates that the proposed method is as accurate as MCMC. The newly developed solution for reliability modeling and assessment of NC machine tools under small-sample data is easy, practical, and highly suitable for widespread application in the engineering field; in addition, the solution does not reduce accuracy.
文摘试运行期间平均故障间隔时间(mean time between failures,MTBF)是反映风电机组可靠性的重要指标,但由于此期间的运行故障数据样本少且故障停机随机性较强,现有MTBF分析方法的误差较大。针对此种小样本估计问题和故障的随机性,提出了一种利用多台机组运行信息的MTBF估计方法。其基本思路是:根据风电机组安装及其故障数据的特点,构造具有相同配置的多台故障停机的随机截尾数据,对机组的可靠度进行Kaplan-Meier非参数估计;基于这种初步估计结果,再进行二参数威布尔(Weibull)分布拟合,并根据Weibull分布的性质计算得到机组的MTBF。该文利用北方某风电场的试运行数据,对2012年11月投产的20台风电机组进行了MTBF分析计算,结果表明该方法能够有效提高机组试运行期MTBF估计的精度。
