Birnbaum Importance Measure of Multi-state Systems under Epistemic Uncertainty

doi:10.3901/JME.2018.08.223

Abstract

Abstract: Importance measures are effective tools to identify the weak components of multi-state systems from a reliability perspective. Nevertheless, the most reported works on importance measures were based on the premise that the deteriorating processes of systems and its components can be precisely known. In many engineering practices, due to limited data, imprecise information, and unknown failure/degradation mechanisms, it is inevitable that the estimated parameters may contain the epistemic uncertainty. By taking account of the epistemic uncertainty associated with the parameters of multi-state system degradation models, a new Birnbaum importance measure of multi-state systems under epistemic uncertainty is put forth. In the proposed method, the Dempster-Shafer evidence theory is used to quantify the epistemic uncertainty, while the Markov model is utilized to characterize the degradation process of multi-state systems. An illustrative example of a cutter feeding control system of machining tools is presented to demonstrate the impact of the epistemic uncertainty on the importance analysis and ranking of components.

Key words: Dempster-Shafer evidence theory, epistemic uncertainty, importance measure, Markov model, multi-state systems

CLC Number:

TB114

XIAHOU Tangfan, LIU Yu, ZHANG Haodong, ZHANG Chenglin. Birnbaum Importance Measure of Multi-state Systems under Epistemic Uncertainty[J]. Journal of Mechanical Engineering, 2018, 54(8): 223-232.

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