A new modeling approach to the safety evaluation of N-modular redundant computer systems in presence of imperfect maintenance
This work addresses safety evaluation for critical control systems, but it is incremental as it builds on existing formal modeling techniques.
The paper tackled the problem of evaluating the safety of N-modular redundant computer systems under imperfect maintenance by developing a compositional multiformalism modeling approach combining Bayesian Networks and Continuous Time Markov Chains, resulting in a method that promotes model reuse and tool interchangeability.
A large number of safety-critical control systems are based on N-modular redundant architectures, using majority voters on the outputs of independent computation units. In order to assess the compliance of these architectures with international safety standards, the frequency of hazardous failures must be analyzed by developing and solving proper formal models. Furthermore, the impact of maintenance faults has to be considered, since imperfect maintenance may degrade the safety integrity level of the system. In this paper we present both a failure model for voting architectures based on Bayesian Networks and a maintenance model based on Continuous Time Markov Chains, and we propose to combine them according to a compositional multiformalism modeling approach in order to analyze the impact of imperfect maintenance on the system safety. We also show how the proposed approach promotes the reuse and the interchange of models as well the interchange of solving tools.