002887262 001__ 2887262
002887262 005__ 20241113111500.0
002887262 0248_ $$aoai:cds.cern.ch:2887262$$pcerncds:FULLTEXT$$pcerncds:THESES$$pcerncds:CERN:FULLTEXT$$pINIS$$pcerncds:CERN
002887262 037__ $$aCERN-THESIS-2023-321
002887262 035__ $$9INSPIRE$$a2751466
002887262 041__ $$aeng
002887262 100__ $$0AUTHOR|(CDS)2313876$$0AUTHOR|(SzGeCERN)829519$$aObermair, [email protected]$$uGraz, Tech. U.
002887262 245__ $$aInterpretable Fault Prediction for CERN Energy Frontier Colliders
002887262 269__ $$c19/01/2024
002887262 300__ $$a151 p
002887262 500__ $$aPresented 19 Jan 2024
002887262 502__ $$aPhD$$bGraz, Tech. U.$$c2023
002887262 520__ $$aThe Large Hadron Collider (LHC) is the world’s highest energy particle collider, which has already delivered data for numerous physical discoveries. To continue this quest for discovering new physics, the Compact Linear Collider (CLIC) and the Future Circular Collider (FCC) aim to push the boundaries of fundamental physics at high collision energies. However, as their power, size, and complexity increases, so does the risk of failures and their associated downtime. Fault prediction is a way to minimize downtime by fixing faults in scheduled maintenance intervals before they occur. In the LHC, such fault prediction methods have been supporting system experts to decrease downtime since its start in 2008/9. There are many different scenarios of faults. Each of them occurs rarely, which is why the predictions cannot be validated by statistical tests alone. Nonetheless, the methods work reliably as their predictions are based on known fault indicators which are validated by experts. To use Machine Learning (ML) methods for fault prediction, the same approach is required: Predictions must be interpreted and validated by experts. Demonstrating the predictive capabilities of ML, this thesis presents three approaches for interpretable fault prediction. Firstly, a novel autoencoder-based method for explaining fault predictions to system experts is proposed. A survey of 73 potential users confirms its effectiveness when compared to two other popular methods. This explanation method is then used to interpret ML-based breakdown predictions in radio frequency cavities. The interpretation reveals that a pattern in the emitted electrons following an initial breakdown is closely related to the probability of another breakdown occurring shortly thereafter. This explanation is consistent with the findings of recent research. Secondly, non-negative matrix factorization, a ML method that is designed to be interpretable, is used to detect normal and abnormal behavior in the LHC main dipole magnets. Five dipole magnets with abnormal behavior are identified, of which one was confirmed to be damaged. Thirdly, a hybrid method is proposed, that allows experts to rely on their existing tools and still benefit from non-interpretable ML. The method is tested to predict faults in a protection system of the LHC main dipole magnets. The method captured 113 out of 116 faults, while only 99 out of 116 faults were captured with the existing tool. The presented approaches demonstrate the strength of interpretable ML for contributing to reliable operation of next-generation particle accelerators. Its applicability extends to numerous other collider components, including radio frequency cavities and dipole magnets in the FCC.
002887262 536__ $$aCERN Doctoral Student Program
002887262 595__ $$aCERN EDS
002887262 65017 $$2SzGeCERN$$aEngineering
002887262 65017 $$2SzGeCERN$$aAccelerators and Storage Rings
002887262 690C_ $$aCERN
002887262 690C_ $$aTHESIS
002887262 693__ $$aCERN LHC
002887262 701__ $$aWollmann, Daniel$$edir.$$uCERN
002887262 701__ $$aPernkopf, Franz$$edir.$$uGraz, Tech. U.
002887262 710__ $$5TE
002887262 859__ [email protected]
002887262 8564_ $$82506454$$s13407932$$uhttps://cds.cern.ch/record/2887262/files/CERN-THESIS-2023-321.pdf
002887262 916__ $$sn$$w202404$$ya2024
002887262 963__ $$aPUBLIC
002887262 960__ $$a14
002887262 980__ $$aTHESIS