An algorithm for the computationally efficient deductive implementation of the Markov/Cell-to-Cell-Mapping Technique for risk significant scenario identification

• A backtracking algorithm is proposed for deductive implementation of the Markov/CCMT.
• The algorithm is computationally efficient in risk significant scenario identification.
• The algorithm is scalable to larger system.

A backtracking algorithm is proposed for the computationally efficient diagnostic/deductive implementation of the Markov/Cell-to-Cell-Mapping Technique (CCMT). Using a probabilistic mapping of the discretized system space onto itself in discrete time that can account for both epistemic and aleatory uncertainties on a phenomenologically consistent platform, Markov/CCMT allows quantification of probabilistic system evolution in time, as well as tracing of fault propagation throughout the system. The algorithm is illustrated using an example level control system and by identifying possible sequential pathways and risk significant scenarios for a given failure mode of the system. The algorithm allows incremental verification of the fidelity of the model used to represent the physics without increased memory requirements. The results show that the algorithm is scalable to larger systems.