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@inproceedings{das:ipac2021-mopab077,
author = {A. Das and M. Borland and L. Emery and X. Huang and D.F. Ratner and H. Shang and G. Shen and R.M. Smith and G.M. Wang},
% author = {A. Das and M. Borland and L. Emery and X. Huang and D.F. Ratner and H. Shang and others},
% author = {A. Das and others},
title = {{Anomaly Detection in Accelerator Facilities Using Machine Learning}},
booktitle = {Proc. IPAC'21},
pages = {304--307},
eid = {MOPAB077},
language = {english},
keywords = {power-supply, operation, GUI, experiment, detector},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-MOPAB077},
url = {https://jacow.org/ipac2021/papers/mopab077.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-MOPAB077},
abstract = {{Synchrotron light sources are user facilities and usually run about 5000 hours per year to support many beamlines operations in parallel. Reliability is a key parameter to evaluate machine performance. Even many facilities have achieved >95% beam reliability, there are still many hours of unscheduled downtime and every hour lost is a waste of operation costs along with a big impact on individual scheduled user experiments. Preventive maintenance on subsystems and quick recovery from machine trips are the basic strategies to achieve high reliability, which heavily depends on experts’ dedication. Recently, SLAC, APS, and NSLS-II collaborated to develop machine-learning-based approaches aiming to solve both situations, hardware failure prediction and machine failure diagnosis to find the root sources. In this paper, we report our facility operation status, development progress, and plans.}},
}