A Heuristic Methodology for Efficient Reduction of Large Multistate Event Trees

This work proposes a new methodology for the management of event tree information used in the quantitative risk assessment of complex systems. The size of event trees increases exponentially with the number of system components and the number of states that each component can be found in. Their reduction to a manageable set of events can facilitate risk quantification and safety optimization tasks. The proposed method launches a deductive exploitation of the event space, to generate reduced event trees for large multistate systems. The approach consists in the simultaneous treatment of large subsets of the tree, rather than focusing on the given single components of the system and getting trapped into guesses on their structural arrangement. 1. Introduction For a given system, the scope of quantitative risk assessment is to investigate the circumstances giving rise to different modes of system operation and to quantify the risk for each operation mode. A system can be comprised of hardware, software, humans, or organizational components [1]. Each component can be found in various states of operation, leading to multiple modes of failure and normal operation for the overall system. Once this mapping of component states to system outcomes is known, it is theoretically possible to quantify the risks for different operation modes to occur, given the occurrence probabilities for all the component states [2, 3]. The computational effort and the memory requirements for risk evaluations increase exponentially as the system components and the number of component states increases. Exact calculations for binary systems are achieved faster by employing binary decision diagrams to effectively organize the evaluation procedure [4]. Since the logic behind multistate systems is not Boolean, multistate behavior cannot be represented by binary models without introducing additional variables and constraints [5]. Rocco and Muselli [6] developed a methodology based on machine-learning and hamming clustering to address multistate systems and any success criterion. The required computational resources can be reduced using approximate risk estimations [7] or criticality analysis [8]. Event trees represent the combination of component states leading to each mode of system operation. Quantification of event trees enables faster exact risk evaluation [2] and is not limited to binary or two-terminal systems, it is, however, very computationally intensive. Clearly, implementation of event tree quantification to systems with many components in multiple states needs to be preceded by