JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{lobach:napac2022-tupa29,
author = {I. Lobach and M. Borland and A. Diaw and J.P. Edelen and G.I. Fystro and A. Sannibale and Y. Sun},
% author = {I. Lobach and M. Borland and A. Diaw and J.P. Edelen and G.I. Fystro and A. Sannibale and others},
% author = {I. Lobach and others},
title = {{Machine Learning for Predicting Power Supply Trips in Storage Rings}},
& booktitle = {Proc. NAPAC'22},
booktitle = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
pages = {413--416},
eid = {TUPA29},
language = {english},
keywords = {storage-ring, power-supply, network, quadrupole, sextupole},
venue = {Albuquerque, NM, USA},
series = {International Particle Accelerator Conference},
number = {5},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2022},
issn = {2673-7000},
isbn = {978-3-95450-232-5},
doi = {10.18429/JACoW-NAPAC2022-TUPA29},
url = {https://jacow.org/napac2022/papers/tupa29.pdf},
abstract = {{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).}},
}