Causality and Temporal Dependencies in the Design of Fault Management Systems

TL;DR

This paper presents a formal framework for fault management in safety-critical systems, focusing on causality, diagnosability, fault detection, and propagation analysis using fault trees and Timed Failure Propagation Graphs.

Contribution

It introduces a formal approach to specify and analyze diagnosability and fault detection, incorporating recent advances in fault propagation analysis with TFPGs.

Findings

Formal framework for diagnosability analysis

Design methodology for fault detection and identification

Application of TFPGs for fault propagation analysis

Abstract

Reasoning about causes and effects naturally arises in the engineering of safety-critical systems. A classical example is Fault Tree Analysis, a deductive technique used for system safety assessment, whereby an undesired state is reduced to the set of its immediate causes. The design of fault management systems also requires reasoning on causality relationships. In particular, a fail-operational system needs to ensure timely detection and identification of faults, i.e. recognize the occurrence of run-time faults through their observable effects on the system. Even more complex scenarios arise when multiple faults are involved and may interact in subtle ways. In this work, we propose a formal approach to fault management for complex systems. We first introduce the notions of fault tree and minimal cut sets. We then present a formal framework for the specification and analysis of diagnosability, and for the design of fault detection and identification (FDI) components. Finally, we review recent advances in fault propagation analysis, based on the Timed Failure Propagation Graphs (TFPG) formalism.