Interpretable Anomaly Detection in the LHC Main Dipole Circuits With Nonnegative Matrix Factorization

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. Non-negative Matrix Factorization is used to model the recorded frequency spectra as common sub-spectra, 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 sub-spectra 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 sub-spectra characteristics, they are also treated as potentially critical. These results are essential for preparing targeted magnet measurements and may lead to preventive replacements.