A critical infrastructure is a complex interconnected system of systems providing basic and essential services to support the operation of particle accelerators but also industries and households for which they must guarantee high reliability of critical functions.
Model-based approaches are usually adopted to provide an early identification of failures and to reveal hidden dependencies among subsystems. System models are complex and require constant updating to be reactive to system changes and real operating conditions, wear and aging. The interconnections between the different systems and the functional dependencies between their components are in many cases modified at both physical and functional levels while their degraded performances impact the overall system availability and reliability.
A novel approach is proposed which combines model-based and Big Data analytics by machine learning techniques to extract descriptive and predictive models directly from data. The objective is to foresee and react in time to failures to reduce downtimes as well as to optimize maintenance and operation costs.
The Computer-Aided System for critical infrastructure Operation (CASO) is designed to significantly and efficiently enhance the quality, safety, reliability and availability of critical infrastructures.
We report on the design of CASO, its implementation and on the preliminary results inferred on historical and live stream data recorded from CERN’s technical infrastructure. Proposal for the full deployment and expected long-term capabilities will also be discussed.