| Title |
Anomaly Detection by Principal Component Analysis and Autoencoder Approach |
| Authors |
- Y.P. Sun
ANL, Lemont, Illinois, USA
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| Abstract |
Several different approach are employed to identify the abnormal events in some Advanced Photon Source (APS) operation archived dataset, where dimensionality reduction are performed by either principal component analysis or autoencoder artificial neural network. It is observed that the APS stored beam dump event, which is triggered by magnet power supply fault, may be predicted by analyzing the magnets capacitor temperatures dataset. There is reasonable agreement among two principal component analysis based approaches and the autoencoder artificial neural network approach, on predicting future overall system fault which may result in a stored beam dump in the APS storage ring.
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| Funding |
The work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. |
| Paper |
download TUPAB061.PDF [0.669 MB / 3 pages] |
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| 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 |
22 May 2021 |
| Accepted |
18 June 2021 |
| Issue Date |
19 August 2021 |
| DOI |
doi:10.18429/JACoW-IPAC2021-TUPAB061 |
| Pages |
1502-1504 |
| 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. |
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