IPAC2021 - List of Authors (Das, A.)

Paper	Title	Page
MOPAB077	Anomaly Detection in Accelerator Facilities Using Machine Learning	304
	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. Wang BNL, Upton, New York, USA
	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.
	Poster MOPAB077 [1.240 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB077
About •	paper received ※ 16 May 2021 paper accepted ※ 14 June 2021 issue date ※ 01 September 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Anomaly Detection in Accelerator Facilities Using Machine Learning

304

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. Wang
BNL, Upton, New York, USA

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.

Poster MOPAB077 [1.240 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB077

About •

paper received ※ 16 May 2021 paper accepted ※ 14 June 2021 issue date ※ 01 September 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)