Fuzzy Fault Trees: the Fast and the Formal

TL;DR

This paper introduces a rigorous fuzzy logic framework for fault tree analysis that efficiently manages uncertainty, adaptable to existing algorithms, and validated on benchmark fault trees.

Contribution

It develops a formal fuzzy fault tree analysis method that is computationally efficient and compatible with existing algorithms, enhancing uncertainty handling in reliability analysis.

Findings

Algorithms are adaptable to fuzzy fault trees.

Framework is computationally efficient.

Validated on synthetic fault tree benchmarks.

Abstract

We provide a rigorous framework for handling uncertainty in quantitative fault tree analysis based on fuzzy theory. We show that any algorithm for fault tree unreliability analysis can be adapted to this framework in a fully general and computationally efficient manner. This result crucially leverages both the alpha-cut representation of fuzzy numbers and the coherence property of fault trees. We evaluate our algorithms on an established benchmark of synthetic fault trees, demonstrating their practical effectiveness.