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@inproceedings{obermair:ipac2021-mopab345,
author = {C. Obermair and A. Apollonio and Z. Charifoulline and M. Maciejewski and F. Pernkopf and A.P. Verweij},
title = {{Machine Learning with a Hybrid Model for Monitoring of the Protection Systems of the LHC}},
booktitle = {Proc. IPAC'21},
pages = {1072--1075},
eid = {MOPAB345},
language = {english},
keywords = {monitoring, operation, superconducting-magnet, dipole, machine-protect},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-MOPAB345},
url = {https://jacow.org/ipac2021/papers/mopab345.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-MOPAB345},
abstract = {{The LHC is the world’s largest particle accelerator and uses a complex set of sophisticated and highly reliable machine protection systems to ensure a safe operation with high availability for particle physics production. The data gathered during several years of successful operation allow the use of data-driven methods to assist experts in finding anomalies in the behavior of those protection systems. In this paper, we derive a model that can extend the existing signal monitoring applications for the LHC protection systems with machine learning. Our hybrid model combines an existing threshold-based system with a SVM by using signals, manually validated by experts. Even with a limited amount of data, the SVM learns to integrate the expert knowledge and contributes to a better classification of safety-critical signals. Using this approach, we analyze historical signals of quench heaters, which are an important part of the quench protection system for superconducting magnets. Particularly, it is possible to incorporate expert decisions into the classification process and to improve the failure detection rate of the existing quench heater discharge analysis tool.}},
}