JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{einstein-curtis:icalepcs2023-tupdp117,
author = {J.A. Einstein-Curtis and K.A. Drees and J.P. Edelen and M.C. Kilpatrick and J.S. Laster and R. O’Rourke and M. Valette},
% author = {J.A. Einstein-Curtis and K.A. Drees and J.P. Edelen and M.C. Kilpatrick and J.S. Laster and R. O’Rourke and others},
% author = {J.A. Einstein-Curtis and others},
title = {{Classification and Prediction of Superconducting Magnet Quenches}},
% booktitle = {Proc. ICALEPCS'23},
booktitle = {Proc. 19th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'23)},
eventdate = {2023-10-09/2023-10-13},
pages = {856--859},
paper = {TUPDP117},
language = {english},
keywords = {power-supply, superconducting-magnet, GUI, operation, experiment},
venue = {Cape Town, South Africa},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {19},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {02},
year = {2024},
issn = {2226-0358},
isbn = {978-3-95450-238-7},
doi = {10.18429/JACoW-ICALEPCS2023-TUPDP117},
url = {https://jacow.org/icalepcs2023/papers/tupdp117.pdf},
abstract = {{Robust and reliable quench detection for superconducting magnets is increasingly important as facilities push the boundaries of intensity and operational runtime. RadiaSoft has been working with Brookhaven National Lab on quench detection and prediction for superconducting magnets installed in the RHIC storage rings. This project has analyzed several years of power supply and beam position monitor data to train automated classification tools and automated quench precursor determination based on input sequences. Classification was performed using supervised multilayer perceptron and boosted decision tree architectures, while models of the expected operation of the ring were developed using a variety of autoencoder architectures. We have continued efforts to maximize area under the receiver operating characteristic curve for the multiple classification problem of real quench, fake quench, and no-quench events. We have also begun work on long short-term memory (LSTM) and other recurrent architectures for quench prediction. Examinations of future work utilizing more robust architectures, such as variational autoencoders and Siamese models, as well as methods necessary for uncertainty quantification will be discussed. }},
}