Yes / Critical technological systems exhibit complex dynamic characteristics such as time-dependent
behavior, functional dependencies among events, sequencing and priority of causes that may alter the effects
of failure. Dynamic fault trees (DFTs) have been used in the past to model the failure logic of such systems,
but the quantitative analysis of DFTs has assumed the existence of precise failure data and statistical
independence among events, which are unrealistic assumptions. In this paper, we propose an improved
approach to reliability analysis of dynamic systems, allowing for uncertain failure data and statistical and
stochastic dependencies among events. In the proposed framework, DFTs are used for dynamic failure
modeling. Quantitative evaluation of DFTs is performed by converting them into generalized stochastic Petri
nets. When failure data are unavailable, expert judgment and fuzzy set theory are used to obtain reasonable
estimates. The approach is demonstrated on a simplified model of a cardiac assist system. / DEIS H2020 Project under Grant 732242.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/17425 |
| Date | 18 October 2019 |
| Creators | Kabir, Sohag, Yazdi, M., Aizpurua, J.I., Papadopoulos, Y. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| Rights | This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ |
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