Causality and Temporal Dependencies in the Design of Fault Management Systems
This work addresses the need for reliable fault management in complex, safety-critical systems, but it appears incremental as it builds on existing formalisms like fault trees and TFPG.
The paper tackles the problem of designing fault management systems for safety-critical applications by proposing a formal approach that includes specifying and analyzing diagnosability, and designing fault detection and identification components, with a review of recent advances in fault propagation analysis using Timed Failure Propagation Graphs.
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.