  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.
