Probabilistic SIL Verification of a Synthesized Fault-Tolerant Model for Reliable Safety Assessment

In safety-critical domains such as industrial processes and automotive systems, accidents can have serious consequences, including the potential loss of human life. To mitigate risks associated with hazards, these systems must incorporate robust safety capabilities. International standards, such as IEC 61508, mandate the implementation of safety functions and require their verification through SIL (Safety Integrity Level) assessments. However, performing accurate SIL verification remains challenging due to the uncertainty of real-world data, which can be influenced by factors such as temperature variations (i.e., heating effects), environmental stressors, and component aging. In particular, failure rates, which serve as fundamental data for SIL verification, are typically assumed to be constant values derived under ideal conditions. But in practice, these rates vary over time due to the progressive degradation of system components, leading to potential discrepancies between theoretical safety assessments and real-world reliability. To address this issue, researchers have explored Markov models as a dynamic approach for improving SIL verification. In this paper, we propose a novel approach based on Markov; constructing one of the fault-tolerant architecture, 2oo3 (2-out-of-3), as a model and verifying SIL using probabilistic model checking. Our approach synthesizes the model to prevent systematic faults, such as human errors, and enhances verification accuracy. To validate our approach, we compare the results of the probabilistic model checking performed on our synthesized model with those obtained from a model without synthesis. The results demonstrate that the synthesized model exhibits the smallest difference rate and a more predictable variation from the target values specified in the IEC 61508 standard for SIL verification. Experimental results confirm that the proposed method enhances the reliability of SIL verification, increasing confidence in safety assessments.