IPAC2021 - List of Authors (Pappas, G.C.)

Paper	Title	Page
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 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)