Compositional Performance Evaluation with Importance Measures

Xibin Zhao, Shubin Si, Hongyan Dui, Zhiqiang Cai, Junbo Wang and Xiaoyu Song

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 24, 5240-5253

Abstract: Importance measures are used to identify weak components and/or states in a system based on the component state random variables, which seem to be inadequate to show the corresponding actual situations. By contrast, the performance random variables own significant practical meanings and eliminate the subjectivity and limitation of state division and definition in many actual situations. In this paper, instead of state random variables, the performance stochastic processes are used for modeling all the components and the entire system, and the integrated importance measure (IIM) for the performance random variables are extended. The generalized IIM evaluates the contribution of component performance to the desired level of system performance. A case study of an oil transmission system is used to illustrate the effectiveness of our approach with importance measures.

Date: 2015
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DOI: 10.1080/03610926.2013.815207

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