Modelling uncertainty in fault tree analyses using evidence theory

Abstract

The Dempster—Shafer Theory of Evidence (DST) has been considered as an alternative to probabilistic modelling if both a large amount of uncertainty and a conservative treatment of this uncertainty are necessary. Both requirements are normally met in early design stages. Expert estimates replace field data and hardly any accurate test results are available. Therefore, a conservative uncertainty treatment is beneficial to assure a reliable and safe design. The present paper explores the applicability of DST which merges interval-based and probabilistic uncertainty modelling on a fault tree analysis from the automotive area. The system under investigation, an automatic transmission from the ZF AS Tronic series is still in the development stage. Expert estimates and the Monte Carlo propagation of the resulting mass function through the system model are used to obtain the uncertainty on the system failure probability. An exploratory sensitivity based on a non-specifity measure indicates which components contribute to the overall model uncertainty. The results are used to predict if the system complies with a given target failure measure.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 17 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.