Efficient reliability analysis of complex systems in consideration of imprecision
- authored by
- Julian Salomon, Niklas Winnewisser, Pengfei Wei, Matteo Broggi, Michael Beer
- 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.
- Organisation(s)
-
Institute for Risk and Reliability
CRC 871 Regeneration of Complex Capital Goods
- External Organisation(s)
-
Northwestern Polytechnical University
University of Liverpool
Tongji University
International Joint Research Center for Engineering Reliability and Stochastic Mechanics
- Type
- Article
- Journal
- Reliability engineering & system safety
- Volume
- 216
- ISSN
- 0951-8320
- Publication date
- 12.2021
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality, Industrial and Manufacturing Engineering
- Electronic version(s)
-
https://doi.org/10.1016/j.ress.2021.107972 (Access:
Open)
