System-Level Radiation Effects Modeling using Temporal Fault Trees

System-level radiation effects are analyzed with a time-dependent probabilistic risk assessment (PRA) methodology. PRA allows for determining the risk of radiation effects in a system based on component-level models informed by test data and dynamic environment models. Failure modes characterized through testing can be predicted over a mission trajectory and propagated using fault tree analysis. The resulting system model provides reliability engineers and mission planners with useful metrics on system performance through hazardous radiation environments, identifying which failure modes and mission phases have the most risk. Here, a case study of an SRAM-based particle flux sensor payload is used to demonstrate the PRA methodology and to harden the payload against the dominant failure modes. Component models capturing total ionizing dose and single-event effects failure modes are propagated through a system fault tree. A Bayesian network was used to identify the payload's most essential components and failure modes. Finally, the analytical solution was verified by Monte Carlo simulation, and the results were used to propose a hardened system, increasing the median mission lifetime by 1,500 %.