Efficient reliability analysis of complex systems in consideration of imprecision

Salomon, Julian, Winnewisser, Niklas, Wei, Pengfei, Broggi, Matteo and Beer, Michael ORCID: 0000-0002-0611-0345 (2021) Efficient reliability analysis of complex systems in consideration of imprecision. Reliability Engineering & System Safety, 216. p. 107972.

Text
efficient systems reliability analysis.pdf - Author Accepted Manuscript
Download (1MB) | Preview

Official URL: http://dx.doi.org/10.1016/j.ress.2021.107972

Abstract

In this work, the reliability of complex systems under consideration of imprecision is addressed. By joining two methods coming from different fields, namely, structural reliability and system reliability, a novel methodology is derived. The concepts of survival signature, fuzzy probability theory and the two versions of non-intrusive stochastic simulation (NISS) methods are adapted and merged, providing an efficient approach to quantify the reliability of complex systems taking into account the whole uncertainty spectrum. The new approach combines both of the advantageous characteristics of its two original components: 1. a significant reduction of the computational effort due to the separation property of the survival signature, i.e., once the system structure has been computed, any possible characterization of the probabilistic part can be tested with no need to recompute the structure and 2. a dramatically reduced sample size due to the adapted NISS methods, for which only a single stochastic simulation is required, avoiding the double loop simulations traditionally employed. Beyond the merging of the theoretical aspects, the approach is employed to analyze a functional model of an axial compressor and an arbitrary complex system, providing accurate results and demonstrating efficiency and broad applicability.

Item Type:	Article
Uncontrolled Keywords:	Survival signature, System reliability, Complex systems, Reliability analysis, Epistemic uncertainty, Imprecision, Fuzzy probabilities, Extended Monte Carlo methods, Non-intrusive imprecise stochastic simulation
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	28 Sep 2021 07:23
Last Modified:	18 Jan 2023 21:28
DOI:	10.1016/j.ress.2021.107972
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3138481