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BiBTeX citation export for WEPA40: The L-CAPE Project at FNAL

  author       = {M. Jain and V.C. Amatya and B.F. Harrison and K.J. Hazelwood and G.U. Panapitiya and W. Pellico and B.A. Schupbach and K. Seiya and J.M. St. John and J.F. Strube},
% author       = {M. Jain and V.C. Amatya and B.F. Harrison and K.J. Hazelwood and G.U. Panapitiya and W. Pellico and others},
% author       = {M. Jain and others},
  title        = {{The L-CAPE Project at FNAL}},
& booktitle    = {Proc. NAPAC'22},
  booktitle    = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
  pages        = {719--722},
  eid          = {WEPA40},
  language     = {english},
  keywords     = {controls, operation, linac, network, alignment},
  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-WEPA40},
  url          = {https://jacow.org/napac2022/papers/wepa40.pdf},
  abstract     = {{The controls system at FNAL records data asynchronously from several thousand Linac devices at their respective cadences, ranging from 15Hz down to once per minute. In case of downtimes, current operations are mostly reactive, investigating the cause of an outage and labeling it after the fact. However, as one of the most upstream systems at the FNAL accelerator complex, the Linac’s foreknowledge of an impending downtime as well as its duration could prompt downstream systems to go into standby, potentially leading to energy savings. The goals of the Linac Condition Anomaly Prediction of Emergence (L-CAPE) project that started in late 2020 are (1) to apply data-analytic methods to improve the information that is available to operators in the control room, and (2) to use machine learning to automate the labeling of outage types as they occur and discover patterns in the data that could lead to the prediction of outages. We present an overview of the challenges in dealing with time-series data from 2000+ devices, our approach to developing an ML-based automated outage labeling system, and the status of augmenting operations by identifying the most likely devices predicting an outage.}},