Description

Fault trees are a popular model in risk analysis: they describe how failures propagate from components to system level. Due to measures like redundancy, not every single component failure automatically leads to a failure at system level. Thus, fault tree leaves model the system’s components, and gates (e.g., AND, OR, priority-AND) model how failures propagate.

A wide number of techniques is available to analyse fault trees, both in a qualitative (cut and path sets) and in a quantitative way, computing the probability that the system has failed in a certain time frame.

Quantitative analysis, however, is often too precise: the input numbers (eg the probabilities at the leaves of the tree) are often not known precisely. Hence, the outcomes of the computation are not precise either. Fuzzy logic is a tool that can formally reason about notions of vagueness, and can therefore be of help to get grip on the uncertainties present in quantitative fault tree analysis.

Goal of this project is to develop a fuzzy framework for fault trees, and to implement fuzzy algorithms in a tool and evaluate the usefulness of the outcomes on a few case studies or examples

TASKS

• Understand fault trees and fuzzy logic
• Reformulate existing fault tree algorithm in terms of fuzzy logic
• Implement fuzzy algorithms
• Evaluate the effectiveness of the framework