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RIS citation export for TUPA29: Machine Learning for Predicting Power Supply Trips in Storage Rings

TY  - CONF
AU  - Lobach, I.
AU  - Borland, M.
AU  - Diaw, A.
AU  - Edelen, J.P.
AU  - Fystro, G.I.
AU  - Sannibale, A.
AU  - Sun, Y.
ED  - Biedron, Sandra
ED  - Simakov, Evgenya
ED  - Milton, Stephen
ED  - Anisimov, Petr M.
ED  - Schaa, Volker R.W.
TI  - Machine Learning for Predicting Power Supply Trips in Storage Rings
J2  - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022
CY  - Albuquerque, NM, USA
T2  - International Particle Accelerator Conference
T3  - 5
LA  - english
AB  - In the Advanced Photon Source (APS) storage ring at Argonne National Lab, trips in the magnet power supplies (PSs) lead to a complete electron beam loss a few times a year. This results in unexpected interruptions of the users’ experiments. In this contribution, we investigate the historical data for the last two decades to find precursors for the PS trips that could provide an advance notice for future trips and allow some preventive action by the ring operator or by the PS maintenance team. Various unsupervised anomaly detection models can be trained on the vast amounts of available reference data from the beamtime periods that ended with an intentional beam dump. We find that such models can sometimes detect trip precursors in PS currents, voltages, and in the temperatures of magnets, capacitors and transistors (components of PSs).
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 413
EP  - 416
KW  - storage-ring
KW  - power-supply
KW  - network
KW  - quadrupole
KW  - sextupole
DA  - 2022/10
PY  - 2022
SN  - 2673-7000
SN  - 978-3-95450-232-5
DO  - doi:10.18429/JACoW-NAPAC2022-TUPA29
UR  - https://jacow.org/napac2022/papers/tupa29.pdf
ER  -