| Autor: |
Lazarova-Molnar, Sanja, Niloofar, Parisa, Barta, Gabor Kevin |
| Přispěvatelé: |
Baraldi, Piero, Di Maio, Francesco, Zio, Enrico |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Lazarova-Molnar, S, Niloofar, P & Barta, G K 2020, Automating Reliability Analysis : Data-driven Learning and Analysis of Multi-state Fault Trees . in P Baraldi, F Di Maio & E Zio (eds), 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020 . Research Publishing Services, pp. 1805-1812, 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020, Venice, Virtual, Italy, 01/11/2020 . https://doi.org/10.3850/978-981-14-8593-0, https://doi.org/10.3850/978-981-14-8593-0 |
| Popis: |
Analysis of failure modes in a system is essential in increasing the reliability of the system. Fault trees model probabilistic causal chains of events that lead to a global system failure. With the emerging availability of data, deriving fault trees from observational data, rather than expert knowledge, would more accurately reflect the true behaviour of a system. Furthermore, systems change their behaviours during their lifetimes. We present an approach for Data-Driven Fault Tree Analysis (DDFTA) of a system with multi-state components which extracts repairable fault trees from time series data, and then analyses the results to estimate the system's reliability measures. Fault trees are typically designed for systems with binary (two states) components, while this is not always the case. There are components with more than two states (multi-state components) in telecommunications, gas and oil production, transportation and electric power distribution. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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