IPAC2021 - List of Authors (Lu, D.)

Paper	Title	Page
TUPAB328	Machine Learning for Time Series Prediction of an Accelerator Beam to Recognize Equipment Malfunction	2272
	C.C. Peters ORNL RAD, Oak Ridge, Tennessee, USA W. Blokland, D.L. Brown, F. Liu, C.D. Long, D. Lu, P. Ramuhalli, D.E. Womble, J. Zhang, A.P. Zhukov ORNL, Oak Ridge, Tennessee, USA
	Funding: ORNL is managed by UT-Battelle, LLC, under contract DE-AC05- 00OR22725 for the U.S. Department of Energy. The Spallation Neutron Source (SNS) is an accelerator based pulsed neutron source based on a 1 GeV pulsed proton Superconducting Radio Frequency (SRF) linear accelerator (linac). Since beginning high power beam operation in 2006 correlations have been found linking abrupt beam loss events to SRF cavity instabilities. With the planned upgrades to double the beam power we expect increased rates of degradation and the importance of minimizing these beam loss events will become ever more important. To further limit degradation, we are developing machine learning approaches to monitor the beam and to detect, predict and prevent beam loss events. Initial research has shown that precursors to beam loss events are detectable. The initial steps are to use ML-based classification to recognize anomalies and to use Long Short-Term Memory (LSTM) autoencoders to predict beam loss. In this paper, we describe recent progress in applying machine learning for recognizing anomalies and predicting beam loss and present initial results of our research using acquired data from different diagnostics and the Machine Protection System (MPS).
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB328
About •	paper received ※ 23 May 2021 paper accepted ※ 28 May 2021 issue date ※ 15 August 2021
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THPAB252	Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators	4303
	G.C. Pappas ORNL RAD, Oak Ridge, Tennessee, USA D. Lu ORNL, Oak Ridge, Tennessee, USA M. Schram JLab, Newport News, Virginia, USA D.L. Vrabie PNNL, Richland, Washington, USA
	Funding: SNS/ORNL is managed by UT-Battelle, LLC, under contract DE-AC05-00OR22725 for the U.S. Department of Energy Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms. * G. Dodson, Approach to Reliable Operations, 26-Dodson_Approach to Reliable Operation-r1.pdf, Feb., 2010.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB252
About •	paper received ※ 18 May 2021 paper accepted ※ 14 July 2021 issue date ※ 29 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Machine Learning for Time Series Prediction of an Accelerator Beam to Recognize Equipment Malfunction

2272

C.C. Peters
ORNL RAD, Oak Ridge, Tennessee, USA
W. Blokland, D.L. Brown, F. Liu, C.D. Long, D. Lu, P. Ramuhalli, D.E. Womble, J. Zhang, A.P. Zhukov
ORNL, Oak Ridge, Tennessee, USA

Funding: ORNL is managed by UT-Battelle, LLC, under contract DE-AC05- 00OR22725 for the U.S. Department of Energy.
The Spallation Neutron Source (SNS) is an accelerator based pulsed neutron source based on a 1 GeV pulsed proton Superconducting Radio Frequency (SRF) linear accelerator (linac). Since beginning high power beam operation in 2006 correlations have been found linking abrupt beam loss events to SRF cavity instabilities. With the planned upgrades to double the beam power we expect increased rates of degradation and the importance of minimizing these beam loss events will become ever more important. To further limit degradation, we are developing machine learning approaches to monitor the beam and to detect, predict and prevent beam loss events. Initial research has shown that precursors to beam loss events are detectable. The initial steps are to use ML-based classification to recognize anomalies and to use Long Short-Term Memory (LSTM) autoencoders to predict beam loss. In this paper, we describe recent progress in applying machine learning for recognizing anomalies and predicting beam loss and present initial results of our research using acquired data from different diagnostics and the Machine Protection System (MPS).

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB328

About •

paper received ※ 23 May 2021 paper accepted ※ 28 May 2021 issue date ※ 15 August 2021

Export •

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

THPAB252

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

4303

G.C. Pappas
ORNL RAD, Oak Ridge, Tennessee, USA
D. Lu
ORNL, Oak Ridge, Tennessee, USA
M. Schram
JLab, Newport News, Virginia, USA
D.L. Vrabie
PNNL, Richland, Washington, USA

Funding: SNS/ORNL is managed by UT-Battelle, LLC, under contract DE-AC05-00OR22725 for the U.S. Department of Energy
Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.
* G. Dodson, Approach to Reliable Operations, 26-Dodson_Approach to Reliable Operation-r1.pdf, Feb., 2010.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB252

About •

paper received ※ 18 May 2021 paper accepted ※ 14 July 2021 issue date ※ 29 August 2021

Export •

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