锻压技术

基于混合威布尔分布的数控折弯机可靠性评估

英文标题：Reliability assessment of CNC bending machine based on mixture Weibull distribution

作者：张根保杨毅刘杰高琦樑

单位：重庆大学

分类号：TH17；TG305

出版年,卷(期):页码：2013,38(6):76-79

摘要：

在收集国产WEHK 110/3100型数控折弯机故障数据的基础上，选用两参数混合威布尔模型进行数据建模，采用最大期望值（EM）算法进行参数估计，通过拟合优度检验确定，机床故障过程服从混合威布尔分布，最后通过计算获得该型机床的可靠性指标。结果表明，机床可靠性指标与企业实际运行非常契合，可作为该型机床可靠性水平的参考，为国内关于数控折弯机的可靠性评估提供了一种新方法。

A two-parameter mixture Weibull distribution model was established on the basis of failure data gathered from CNC bending machine of WEHK 110/3100,the model was defined by goodness-of-fit test, and the parameter estimate was solved by EM algorithm. Based on the data, the machine breakdowns following Weibull distribution was proved. In the end, reliability index of the CNC bending machine was achieved. The results show that the index is in conformity with actual operations of enterprise, and it is capable to be used as a reference for bending machine reliability, hence maybe it is a new approach to reliability assessment of CNC bending machine.

基金项目：

国家自然科学基金资助项目（51175527;50835008）；国家科技重大专项资助项目“高档数控机床与基础制造装备”资助项目（2010ZX04014-015；2011ZX04003-031；2012ZX04011-031）

参考文献：

［1］李研. 国内外数控车床可靠性对比分析[D]. 长春: 吉林大学, 2006.Li Y. Domestic and Foreign-made CNC Lathe Reliability Contrast Analysis[D]. Changchun: Jilin University, 2006.
［2］Jia Y Z, Wang M L, Jia Z X, Probability distribution of machining center failure [J]. Reliability Engineering and System Safety, 1995, 50(1): 121-125.
［3］Jiang S, Kececioglu D. Graphical representation of two mixed-Weibull distributions[J]. IEEE Transactions on Reliability, 1992, 41(2): 241-247.
［4］张海波, 贾亚洲, 周广文.数控系统故障间隔时间分布模型的研究[J]. 哈尔滨工业大学学报, 2005, 37(2): 198-200.Zhang H B, Jia Y Z, Zhou G W. Time between failures distribution model of CNC system[J]. Journal of Harbin Institute of Technology, 2005, 37(2): 198-200.
［5］Guo H, Watson S, Tavner P, et al. Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation [J]. Reliability Engineering and System Safety, 2009, 94(6):1057-1063.
［6］Surucu B, Sazak H S. Monitoring reliability for a three-parameter Weibull distribution[J]. Reliability Engineering and System Safety, 2009, 94(2): 503-508.
［7］Wang Z M, Yang J G, Wang G Q. Application of 3-parameter Weibull mixture model for reliability assessment of NC machine tools: a case study [J]. Proceedings of the Institution of Mechanical Engineers,Part C:Journal of Mechanical Engineering Science, 2011,225(11): 2718-2726.
［8］李平. 一类混合模型的似然比检验[J]. 应用概率统计, 2004, 20 (2):179-183.Li P. Likelihood ratio test of a class of mixture model[J]. Chinese Journal of Applied Probability and Statisties , 2004, 20 (2):179-183.
［9］Bucar T, Nagode M, Fajdiga M. Reliability approximation using finite Weibull mixture distributions [J]. Reliability Engineering and System Safety, 2004, 84(3): 241-251.
［10］蒋仁言, 左明健. 可靠性模型与应用[M]. 北京:机械工业出版社, 1999.Jiang R Y, Zuo M J. Reliability Model and Application[M]. Beijing: China Machine Press,1999.
［11］Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm [J]. Journal of the Royal Statistical Society, Series B (Methodological), 1977, 39(1):1-38.
［12］贺国芳. 可靠性数据的收集与分析[M]. 北京:国防工业出版社, 1997.He G F. Collecting and Analysis of Reliability Data[M]. Beijing: National Defense Industry Press, 1997.

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