• CN:11-2187/TH
  • ISSN:0577-6686

›› 2012, Vol. 48 ›› Issue (8): 153-158.

• 论文 • 上一篇    下一篇

可靠性度量中的不确定信息处理

李玲玲;武猛;李志刚   

  1. 河北工业大学电磁场与电器可靠性省部共建重点实验室;天津大学电气工程博士后流动站
  • 发布日期:2012-04-20

Uncertain Information Processing Method in the Reliability Measurement

LI Lingling;WU Meng;LI Zhigang   

  1. Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology Postdoctoral Mobile Research Station of Electrical Engineering, Tianjin University
  • Published:2012-04-20

摘要: 在可靠性度量中,除常见的随机不确定信息,还普遍存在着模糊不确定信息,因此在传统可靠性分析的基础上,基于模糊理论提出一种综合处理随机不确定信息与模糊不确定信息的可靠度计算方法,获得能同时处理概率密度函数与隶属函数的可靠度计算模型;并且在改进型证据理论的基础上对不同来源的可靠性信息进行融合处理,提出两种信息融合方式。研究结果表明,在随机性与模糊性并存的情况下,此可靠性度量模型能够较好地处理随机信息与模糊信息,所得结果有很高的准确性,并且能够动态地反映可靠度的计算机理;信息融合方法能够较好地处理不同来源的可靠性信息,对多源信息情况下的可靠度计算提供一种处理方法。

关键词: 不确定性信息, 隶属函数, 模糊可靠度, 信息融合, 证据理论

Abstract: In the system reliability analysis, in addition to the common random uncertain information, also there is fuzzy uncertain information. On the basis of traditional reliability and fuzzy theory, a kind of reliability calculation method which could handle random uncertain information and fuzzy uncertain information comprehensively is proposed, and reliability calculation model is got, which could process the probability density function and membership function simultaneously. What’s more basic on the improved evidence theory, two kinds of information integration methods are proposed by integrating reliability information from different sources. The study results show that:in the case of both randomness and fuzziness existing, this reliability model could handle random information and fuzzy information more appropriately, besides a higher accuracy, it could also reflect reliability calculating mechanism dynamically; information integration method could process reliability information from different sources in a better way, therefore it is a useful method of calculating reliability form multi-source information.

Key words: Evidence theory, Fuzzy reliability, Information integration, Membership function, Uncertain information

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