STI Publications - View Publication Form #16820

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Publication Information
Title Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators
Abstract 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 statistic
Author(s) G. Pappas, D. Lu, Malachi Schram, D.L. Vrabie
Publication Date August 2021
Document Type Meeting, Proceedings
Primary Institution Oak Ridge National Laboratory, Oak Ridge, TN
Affiliation Comp Sci&Tech (CST) Div / Data Science / Data Science
Funding Source Nuclear Physics (NP), DE-AC05-00OR22725
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-CST-21-3503 OSTI Number: 1828306
LANL Number: Other Number: DOE/OR/23177-5352
Associated with an experiment No
Associated with EIC No
Supported by Jefferson Lab LDRD Funding No
Meeting / Conference
Meeting Name IPAC21
Meeting Date 5/24/2021
Document Subtype Paper (proceedings only)
Proceedings
Title Proceedings of IPAC 2021
Editor(s) Liu Lin, John M. Byrd, Regis Neuenschwander, Renan Picoreti, Volker R. W. Schaa
Publisher JACOW
Refereed No
Page Number 4303-4306
Year 2021
Attachments/Datasets/DOI Link
Document(s)
thpab252.pdf (STI Document)
thpab252.pdf (Accepted Manuscript)
DOI Link
Dataset(s) (none)
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