The design process of complex systems must guarantee not only the functional correctness of the implemented system, but also its safety, dependability, and resilience with respect to run-time faults. To this aim, complex systems implement mechanisms to timely detect components’ faults and to isolate them, before they can propagate and cause system failures. Hence, the design process must characterize the likelihood and severity of faults, identify the set of possible hazards and failure conditions, mitigate possible consequences, and assess the effectiveness of the adopted mitigation measures.

Model-Based Safety Analysis (MBSA) is listed as an acceptable and recommended means of compliance to perform safety assessment in the latest issue of SAE ARP4761A, specifically for analyzing failure propagation. MBSA is based on the adoption of a formal, mathematical model of the system and on a tool-supported methodology to assist the generation of safety artifacts. State-of-the-art tools for MBSA implement functionalities to generate Minimal Cut Sets (MCS) from a fault propagation model and a Top-Level Event (TLE) [IMBSA25, LPNMR22, CAV21]; perform automated fault injection into a behavioral design model to generate the corresponding safety model [FAOC21, TACAS16]; generate Minimal Cut Sets from a fully behavioral dynamical model and a TLE [FAOC21, TACAS16, CAV15a, SCP15]; perform various kind of validation of fault propagation models against behavioral models [IJCAI16, AAAI16, AAAI15].

The objective of this study is to advance the state-of-the-art in failure propagation analysis and safety assessment of complex systems. In particular, it will investigate extensions of existing formalisms to deal with aspects such as the timing of fault propagation, the characterization of transient and sporadic faults, and the analysis of the effectiveness of fault mitigation measures in presence of complex fault patterns. Moreover, this study will investigate the use of fault propagation models for the design of fault detection, isolation and recovery (FDIR) components. To this aim, fault propagation models will be extended with observability information and used to solve problems such as anomaly detection, diagnosis, root-cause analysis, and prognosis. Finally, this study will aim to bridge the gap between fault propagation models and fully behavioral system models used for the design and safety assessment of complex systems.