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中国管理科学

中国管理科学 ›› 2016, Vol. 24 ›› Issue (11): 170-176.doi: 10.16381/j.cnki.issn1003-207x.2016.11.020

• 论文 • 上一篇    

某大型装置现场可靠性的贝叶斯分析

周瑜1, 寇纲2, 尔古打机3   

  1. 1. 内蒙古大学经济管理学院, 内蒙古 呼和浩特 010021;
    2. 西南财经大学工商管理学院, 四川 成都 611130;
    3. 西南民族大学电气工程学院, 四川 成都 610041
  • 收稿日期:2015-06-03 修回日期:2015-10-20 出版日期:2016-11-20 发布日期:2017-01-23
  • 作者简介:寇纲(1975-),男(汉族),江西上饶人,西南财经大学工商管理学院教授,博士,博士生导师,研究方向:商务智能、应急管理,Email:kougang@swufe.edu.cn.
  • 基金资助:

    国家自然科学基金资助项目（71222108，71373216，71471149，71501103）；内蒙古大学高层次人才引进科研启动项目（5145131）；内蒙古自然科学基金资助项目（2015BS0705）

The Bayesian Analysis of Field Reliability for a Repairable System

ZHOU Yu1, KOU Gang2, ERGU Da-ji3   

  1. 1. School of Economics and Management, Inner Mongolia University, Hohhot 010021, China;
    2. School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China;
    3. School of Electrical and Information Engineering, Southwest University for Nationalities Chengdu 610041, China
  • Received:2015-06-03 Revised:2015-10-20 Online:2016-11-20 Published:2017-01-23

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摘要： 可靠性、维修性和可用性是描述复杂装置质量属性的三个重要指标。在某大型装置的现场故障和维修数据的经验分析基础上，运用分段线性模型确定了装置可靠性改进趋势。针对装置现场故障和维修数据的小样本问题，给出了装置现场可靠性和维修性的贝叶斯模型，并用WinBUGS软件对其进行了求解计算。继而分析给出了装置可用性最恰当描述——任务可用度的闭合表达形式。最后，通过与极大似然法进行对比，发现应用贝叶斯方法分析该大型装置现场可靠性更有效。

关键词: 现场可靠性, 维修性, 任务可用度, 贝叶斯

Abstract: Reliability, maintainability and availability are three important attributes to describe the quality of a large-scale device. A certain large-scale device is in the prototype testing phase and clearly exhibits reliability growth. However, the high environmental stress and operation cost result in the difficulties to collect field data. Moreover, the downtime constraint should be considered when the availability analysis is carried out. Therefore, it is very difficult to carry out device reliability, maintainability and availability assessments. In this paper, the empirical analysis is first carried out based on the field failure and maintenance data, then the device reliability improvement trend is determined with a piecewise linear model. For the small sample question of the field failure and maintenance data, the Bayesian method is introduced, and the field reliability and maintainability parameters are calculated by the soft WinBUGS. Then the most appropriate description of the system availability—mission availability with closed expression form is presented. In order to illustrate the effectiveness of the method, the field data from June 4, 2007 to May 17, 2010 has been couected. Through the case study, two reliability change-points are determined and thus the device reliability and maintainability are analyzed with Bayesian method. As a result, the average relative error of Bayesian and maximum likelihood methods are 0.42% and 8.78% respectively. Then it can be concluded that using the Bayesian method to carry out field reliability analysis is more effective compared to the maximum likelihood method.

Key words: field reliability, maintainability, mission availability, bayesian

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