Speaker
Description
At present, the CERN Fire and Gas detection systems involve about 23000 assets and their number is increas-ing rapidly at the same time as the number of equipped installations grows. These assets cover a wide spectrum of technologies, manufacturers, models, parameters, and ages, reflecting the 60 years of CERN history. The use of strict rules and data structures in the declaration of the assets can make a big impact on the overall system main-tainability and therefore on the global reliability of the installation. Organized asset data facilitates the creation of powerful reports that help asset owners and manage-ment address material obsolescence and end-of-life con-cerns with a global perspective.
Historically, preventive maintenance has been used to assure the correct function of the installations. With modern supervision systems, a lot of data is collected and can be used to move from preventive maintenance to-wards data-driven maintenance (predictive). Moreover, it optimizes maintenance cost and increases system availability while maintaining reliability. A prerequisite of this move is a coherence on the assets defined in the asset management system and in the supervision system.
