Offshore system safety and reliability considering microbial influenced multiple failure modes and their interdependencies

The stochastic nature of microbial corrosion creates spatial interdependencies among random corrosion parameters and their failure modes. These interdependencies need to be captured for robust offshore system reliability prediction considering complex multispecies biofilms. This research paper presents a hybrid methodology for the prediction of system reliability, considering multiple failure modes' interdependencies. The methodology integrates the Bayesian Network with Copula-based Monte Carlo (BN-CMC) simulation. The BN captures the dynamic interactions among physio-chemical parameters and microbes to predict the corrosion rate of an offshore system. The random corrosion parameters dependencies and the failure modes that define the performance functions under microbial corrosion are modeled using CMC. The methodology is assessed with an example, and the impact of dynamic interactions of the parameters and their failure modes on the system reliability is investigated. The results reveal that the system's probability of failure differs diversely as the degree of dependencies among the random corrosion parameters and their failure modes increases. The proposed methodology can predict the failure indexes that could aid system integrity management for a sustainable offshore operation experiencing microbial corrosion.