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Efficient treatment of uncertainty in system reliability analysis using importance measures.
49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 76-87 (2019)
The reliability of today's electronic products suffers from a growing variability of failure and ageing effects. In this paper, we investigate a technique for the efficient derivation of uncertainty distributions of system reliability. We assume that a system is composed of unreliable components whose reliabilities are modeled as probability distributions. Existing Monte Carlo (MC) simulation-based techniques, which iteratively select a sample from the probability distributions of the components, often suffer from high execution time and/or poor coverage of the sample space. To avoid the costly re-evaluation of a system reliability during MC simulation, we propose to employ the Taylor expansion of the system reliability function. Moreover, we propose a stratified sampling technique which is based on the fact that the contribution (or importance) of the components on the uncertainty of their system may not be equivalent. This technique finely/coarsely stratifies the probability distribution of the components with high/low contribution. The experimental results show that the proposed technique is more efficient and provides more accurate results compared to previously proposed techniques.
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Publikationstyp Artikel: Journalartikel
Schlagwörter Importance Measure ; Reliability ; Sampling ; Stratified Sampling ; System Design ; Uncertainty Analysis
Konferenztitel 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
Konferzenzdatum 24-27 June 2019
Konferenzort Portland, OR, USA, US
Quellenangaben Seiten: 76-87
Institut(e) Institute of Computational Biology (ICB)