TY - JOUR
T1 - Interpretable Anomaly Detection in the LHC Main Dipole Circuits With Nonnegative Matrix Factorization
AU - Obermair, Christoph
AU - Apollonio, Andrea
AU - Charifoulline, Zinour
AU - Felsberger, Lukas
AU - Janitschke, Marvin
AU - Pernkopf, Franz
AU - Ravaioli, Emmanuele
AU - Verweij, Arjan
AU - Wollmann, Daniel
AU - Wozniak, Mariusz
N1 - Publisher Copyright:
© 2002-2011 IEEE.
PY - 2024/6/1
Y1 - 2024/6/1
N2 - CERN's Large Hadron Collider (LHC), with its eight superconducting main dipole circuits, has been in operation for over a decade. During this time, relevant operational parameters of the circuits, including circuit current, voltages across magnets and their coils, and current to ground, have been recorded. These data allow for a comprehensive analysis of the circuit characteristics, the interaction between their components, and their variation over time. Such insights are essential to understand the state of health of the circuits and to detect and react to hardware fatigue and degradation at an early stage. In this work, a systematic approach is presented to better understand the behavior of the main LHC dipole circuits following fast power aborts. Nonnegative matrix factorization is used to model the recorded frequency spectra as common subspectra by decomposing the recorded data as a linear combination of basis vectors, which are then related to hardware properties. The loss in reconstructing the recorded frequency spectra allows to distinguish between normal and abnormal magnet behavior. In the case of abnormal behavior, the analysis of the subspectra properties enables to infer possible hardware issues. Following this approach, five dipole magnets with abnormal behavior were identified, of which one was confirmed to be damaged. As three of the other four identified magnets share similar subspectra characteristics, they are also treated as potentially critical. These results are essential for preparing targeted magnet measurements and may lead to preventive replacements.
AB - CERN's Large Hadron Collider (LHC), with its eight superconducting main dipole circuits, has been in operation for over a decade. During this time, relevant operational parameters of the circuits, including circuit current, voltages across magnets and their coils, and current to ground, have been recorded. These data allow for a comprehensive analysis of the circuit characteristics, the interaction between their components, and their variation over time. Such insights are essential to understand the state of health of the circuits and to detect and react to hardware fatigue and degradation at an early stage. In this work, a systematic approach is presented to better understand the behavior of the main LHC dipole circuits following fast power aborts. Nonnegative matrix factorization is used to model the recorded frequency spectra as common subspectra by decomposing the recorded data as a linear combination of basis vectors, which are then related to hardware properties. The loss in reconstructing the recorded frequency spectra allows to distinguish between normal and abnormal magnet behavior. In the case of abnormal behavior, the analysis of the subspectra properties enables to infer possible hardware issues. Following this approach, five dipole magnets with abnormal behavior were identified, of which one was confirmed to be damaged. As three of the other four identified magnets share similar subspectra characteristics, they are also treated as potentially critical. These results are essential for preparing targeted magnet measurements and may lead to preventive replacements.
KW - Large Hadron Collider (LHC)
KW - machine learning
KW - nonnegative matrix factorization (NMF)
KW - quench protection
UR - http://www.scopus.com/inward/record.url?scp=85187300458&partnerID=8YFLogxK
U2 - 10.1109/TASC.2024.3363725
DO - 10.1109/TASC.2024.3363725
M3 - Article
AN - SCOPUS:85187300458
SN - 1051-8223
VL - 34
SP - 1
EP - 12
JO - IEEE Transactions on Applied Superconductivity
JF - IEEE Transactions on Applied Superconductivity
IS - 4
M1 - 4004112
ER -