IPAC2018 - List of Authors (Antonello, F.)

Paper	Title	Page
WEPAF079	A Smart Framework for the Availability and Reliability Assessment and Management of Accelerators Technical Facilities	2024
	L. Serio, A. Castellano, U. Gentile CERN, Geneva, Switzerland F. Antonello, P. Baraldi, E. Zio Politecnico di Milano, Milan, Italy
	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.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF079
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Paper

Title

Page

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

2024

L. Serio, A. Castellano, U. Gentile
CERN, Geneva, Switzerland
F. Antonello, P. Baraldi, E. Zio
Politecnico di Milano, Milan, Italy

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.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF079

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)