Probabilistic risk assessment (PRA) is essential to keep e.g. our power plants, trains, drones, medical devices, satellites, and self-driving cars safe and operational within acceptable risk levels. PRA usage is required by e.g. the Federal Aviation Authority (FAA), the Nuclear Regulatory Commission (NRC), in ISO 26262 for autonomous driving or for software development in aerospace systems (by NASA and ESA).
PRA encompasses two steps: risk quantification - through static fault tree analysis (FTA) -, and consequence quantification - through event tree analysis (ETA).
Though highly adopted formalism, static fault trees are very simple formalism – equivalent to boolean logic – and therefore cannot faithfully model fail-operational/fault-tolerant systems having redundancies, (probabilistic) functional dependencies, and temporal ordering among failing components. Similarly, traditional event trees cannot model the decision-making – might be required to mitigate the impact of e.g. accidental events –, and quantify measures like no of casualties, monetary loss, etc.
In collaboration with Twente and RWTH Aachen universities, we developed a fully automatic, scalable, and state-of-the-art tool SAFEST that can faithfully assess the risk of fail-operational/fault-tolerant systems. It goes beyond the capabilities of existing commercial tools in offering more flexible modeling and analyses. It has two modules:
SAFEST is the most complete, effective, and scalable tool
for analyzing static and dynamic fault trees. Static fault tree analysis is competitive with
existing tools. It can check more reliability measures than ever and can check fault trees that
are larger than ever. It can model systems having:
In SAFEST we extend classical event trees with non-deterministic choices/decision-making at states, and allow the addition of state rewards/losses. Moreover, DFTs can be embedded into RETs and provide transition probabilities (of RETs). By analyzing RETs, one can determine:
Because of limited expressiveness, SFTs cannot model dynamic behaviour of systems in which:
ISO 26262:2011 demands rigorous risk assessments in automotive industry:
Rapidly increased usage of AI components in modern systems necessitates a rigorous risk assessment
FTA focuses on computing various dependability metrics, i.e. key performance
indicators that measure how well a system performs. Standard metrics are the
system:
Reliability : the probability that no failure occurred until time T
Conditional Reliability : the probability that no failure occurred
until time T given a component has already failed
Availability : the average percentage of time that a system is
operational
Mean Time to Failure (MTTF): the mean time between failures,
Criticality of components : to what extent does a component failure
contribute to a system failure, etc.
Our tools also handles various extensions that include the cost and impact of
failures.
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