Abstract
Advanced classical Probabilistic Risk Assessment (PRA) effectively combines various methods for quantitative risk evaluation, such as event trees, fault trees, and Bayesian networks. PRA methods and tools provide the means for the qualitative reliability evaluation (e.g., cut sets) and the computation of quantitative reliability metrics (e.g., end states probabilities). Modern safety-critical systems from various industrial domains tend toward a high level of autonomy and demand not only reliability but also resilience, the ability to recover from degraded or failed states. The numerical resilience analysis of such dynamic systems requires more flexible methods. These methods shall enable the analysis of the systems with sophisticated software parts and dynamic feedback loops. A suitable candidate is the Dual-graph Error Propagation Model (DEPM) that can capture nontrivial failure scenarios and dynamic fault-tolerance mechanisms. The DEPM exploits the method for the automatic generation of Markov chain models and the application of probabilistic model checking techniques. Moreover, the DEPM enables the analysis of highly-customizable system resilience metrics, e.g., “the probability of system recovery to a particular state after a specified system failure during a defined time interval.” In this paper, we show how DEPM-based resilience analysis can be integrated with the general PRA methodology for resilience evaluations. The proposed methodology is demonstrated on a safety-critical autonomous UAV system.
High-level Estimation and Exploration of Reliability for Multi-Processor System-on-Chip