A Smart Framework for the Availability and Reliability Assessment and Management of Accelerators Technical Facilities

CERN operates and maintains a large and complex technical infrastructure serving the accelerator complex and experiments detectors. A performance assessment and enhancement framework based on data mining, artificial intelligence and machine-learning algorithms is under development with the objective of structuring, collecting and analyzing systems and equipment operation and failure data, to guide the identification and implementation of adequate corrective, preventive and consolidation interventions. The framework is designed to collect and structure the data, identify and analyze the associated driving events. It develops dynamically functional dependencies and logic trees, descriptive and predictive models to support operation and maintenance activities to improve the reliability and availability of the installations. To validate the performance of the framework and quality of the algorithms several case studies are being carried out. We report on the design, implementation and on the preliminary results inferred on historical and live stream data from CERN's technical infrastructure. Proposal for the full deployment and expected long-term capabilities will also be discussed.