| Title |
Anomaly Detection in Accelerator Facilities Using Machine Learning |
| Authors |
- A. Das
Stanford University, Stanford, California, USA
- M. Borland, L. Emery, X. Huang, H. Shang, G. Shen
ANL, Lemont, Illinois, USA
- D.F. Ratner
SLAC, Menlo Park, California, USA
- R.M. Smith, G.M. Wangpresenter
BNL, Upton, New York, USA
|
| 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.
|
| Paper |
download MOPAB077.PDF [0.781 MB / 4 pages] |
| Poster |
download MOPAB077_POSTER.PDF [1.240 MB] |
| Export |
download ※ BibTeX
※ LaTeX
※ Text/Word
※ RIS
※ EndNote |
| Conference |
IPAC2021 |
| Series |
International Particle Accelerator Conference (12th) |
| Location |
Campinas, SP, Brazil |
| Date |
24-28 May 2021 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-214-1 |
| Online ISSN |
2673-5490 |
| Received |
16 May 2021 |
| Accepted |
14 June 2021 |
| Issue Date |
01 September 2021 |
| DOI |
doi:10.18429/JACoW-IPAC2021-MOPAB077 |
| Pages |
304-307 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
|
