Automating Reliability Analysis: Data-driven Learning and Analysis of Multi-state Fault Trees

Sanja Lazarova-Molnar*, Parisa Niloofar, Gabor Kevin Barta

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Abstrakt

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.

OriginalsprogEngelsk
Titel30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
RedaktørerPiero Baraldi, Francesco Di Maio, Enrico Zio
ForlagResearch Publishing Services
Publikationsdato2020
Sider1805-1812
ISBN (Trykt)9789811485930
ISBN (Elektronisk)9789811485930
DOI
StatusUdgivet - 2020
Begivenhed30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020 - Venice, Virtual, Italien
Varighed: 1. nov. 20205. nov. 2020

Konference

Konference30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
Land/OmrådeItalien
ByVenice, Virtual
Periode01/11/202005/11/2020

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