Efficient reliability analysis of complex systems in consideration of imprecision. (December 2021)

• Record Type: Journal Article
• Authors: Salomon, Julian; Winnewisser, Niklas; Wei, Pengfei; Broggi, Matteo; Beer, Michael
• Is Part Of: Reliability engineering & system safety. Volume 216(2021)
• Publication Date: 2021-12
• Subjects: Survival signature -- System reliability -- Complex systems -- Reliability analysis -- Epistemic uncertainty -- Imprecision -- Fuzzy probabilities -- Extended Monte Carlo methods -- Non-intrusive imprecise stochastic simulation; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.107972
• Languages: English
• ISSNs: 0951-8320

Abstract

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. Highlights: Merging of non-intrusive stochastic simulation (NISS) and survival signature. Reliability analysis: reduced computational effort for repeated model evaluations. Significantly reduced sample size for mixed uncertainty is achieved. Fuzzy probabilities are utilized to propagate the uncertainty comprehensively. A decision-making framework identifying a critical level of imprecision is proposed.