Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions

Jinhua Mi (), Yuhua Cheng (), Yufei Song (), Libing Bai () and Kai Chen ()
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Jinhua Mi: School of Automation Engineering, University of Electronic Science and Technology of China
Yuhua Cheng: School of Automation Engineering, University of Electronic Science and Technology of China
Yufei Song: School of Automation Engineering, University of Electronic Science and Technology of China
Libing Bai: School of Automation Engineering, University of Electronic Science and Technology of China
Kai Chen: School of Automation Engineering, University of Electronic Science and Technology of China

Annals of Operations Research, 2022, vol. 311, issue 1, No 21, 333 pages

Abstract: Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples.

Keywords: Evidence theory; Dynamic evidential networks; Epistemic uncertainty; Multiple life distribution (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10479-019-03211-4

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