ICALEPCS2021 - List of Authors (Donon, Y.)

Paper	Title	Page
THPV041	Innovative Methodology Dedicated to the CERN LHC Cryogenic Valves Based on Modern Algorithm for Fault Detection and Predictive Diagnostics	959
	M. Pezzetti, A. Amodio, Y. Donon, L. Iodice CERN, Geneva, Switzerland P. Arpaia Naples University Federico II, Science and Technology Pole, Napoli, Italy F. Gargiulo University of Naples Federico II, Naples, Italy
	The European Organization for Nuclear Research (CERN) cryogenic infrastructure is composed of many equipment, among them there are the cryogenic valves widely used in the Large Hadron Collider (LHC) cryogenic facility. At present time, diagnostic solutions that can be integrated into the process control systems, capable to identify leak failures in valves bellows, are not available. The authors goal has been the development of a system that allows the detection of helium leaking valves during normal operation using available data extracted from the control system. The design constraints has driven the development towards a solution integrated in the monitoring systems in use, not requiring manual interventions. The methodology presented in this article is based on the extraction of distinctive features (analyzing the data in time and frequency domain) which are exploited in the next phase of machine learning. The aim is to identify a list of candidate valves with a high probability of helium leakage. The proposed methodology, which is at very early stage now, with the evolution of the data set and the iterative approach is aiming toward a cryogenic valves targeted maintenance.
	Poster THPV041 [1.120 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THPV041
About •	Received ※ 06 October 2021 Revised ※ 26 October 2021 Accepted ※ 22 December 2021 Issue date ※ 02 March 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Innovative Methodology Dedicated to the CERN LHC Cryogenic Valves Based on Modern Algorithm for Fault Detection and Predictive Diagnostics

959

M. Pezzetti, A. Amodio, Y. Donon, L. Iodice
CERN, Geneva, Switzerland
P. Arpaia
Naples University Federico II, Science and Technology Pole, Napoli, Italy
F. Gargiulo
University of Naples Federico II, Naples, Italy

The European Organization for Nuclear Research (CERN) cryogenic infrastructure is composed of many equipment, among them there are the cryogenic valves widely used in the Large Hadron Collider (LHC) cryogenic facility. At present time, diagnostic solutions that can be integrated into the process control systems, capable to identify leak failures in valves bellows, are not available. The authors goal has been the development of a system that allows the detection of helium leaking valves during normal operation using available data extracted from the control system. The design constraints has driven the development towards a solution integrated in the monitoring systems in use, not requiring manual interventions. The methodology presented in this article is based on the extraction of distinctive features (analyzing the data in time and frequency domain) which are exploited in the next phase of machine learning. The aim is to identify a list of candidate valves with a high probability of helium leakage. The proposed methodology, which is at very early stage now, with the evolution of the data set and the iterative approach is aiming toward a cryogenic valves targeted maintenance.

Poster THPV041 [1.120 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THPV041

About •

Received ※ 06 October 2021 Revised ※ 26 October 2021 Accepted ※ 22 December 2021 Issue date ※ 02 March 2022

Cite •

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