Publications
Publication Information
| Title | Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators |
| Authors | G. Pappas, D. Lu, Malachi Schram, D.L. Vrabie |
| JLAB number | JLAB-CST-21-3503 |
| LANL number | (None) |
| Other number | DOE/OR/23177-5352 |
| Document Type(s) | (Meeting) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
| Other Funding: | DE-AC05-00OR22725 |
|
Meeting Paper compiled for IPAC21 Proceedings Proceedings of IPAC 2021 Edited By Liu Lin, John M. Byrd, Regis Neuenschwander, Renan Picoreti, Volker R. W. Schaa JACOW (2021) Page(s) 4303-4306 | |
| Publication 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 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. |
| Experiment Numbers: | |
| Group: | Data Science |
| Document: | |
| DOI: | https://doi.org/10.18429/JACoW-IPAC2021-THPAB252 |
| Accepted Manuscript: | thpab252.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |