A Niching Augmented Evolutionary Algorithm for the Identification of Functional Dependencies in Complex Technical Infrastructures From Alarm Data

Functional dependencies in complex technical infrastructures can cause unexpected cascades of failures. Given the complexity and continuous evolution in time of the infrastructure architecture, the identification of functional dependencies using the classical methods of system decomposition and logic analysis is not feasible. Then, we consider the availability of databases of alarm messages and frame the identification of functional dependencies in complex technical infrastructures as an optimization problem whose objective is the maximization of a metric measuring the level of dependence among alarms. A niching-based evolutionary algorithm has been developed to sequentially evolve a population of candidate solutions (group of alarms), maintaining diversity among them thanks to the use of a mechanism of population augmentation. The proposed algorithm is applied to a synthetic database of alarms generated by a complex technical infrastructure simulation model and to a real large-scale database of alarms collected from the particle accelerator of European Organization for Nuclear Research. The proposed algorithm is shown able to identify functional dependencies and to overperform other approaches based on the use of association rule mining algorithms, in terms of capability of extracting rare rules and computational efficiency.