Document Type

Article

Publication Date

2024

DOI

10.1088/2632-2153/ad7ad6

Publication Title

Machine Learning: Science and Technology

Volume

5

Issue

3

Pages

035078 (1-13)

Abstract

Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model's ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

Rights

© 2024 The Authors.

Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License. Any futher distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Data Availability

Article states: "The data cannot be made publicly available upon publication because of the cost of preparing, depositing, and hosting the data would be prohibitive within the terms of this research project. The data that support the findings of this study are available upon reasonable request from the authors."

Original Publication Citation

Rahman, M. M., Carpenter, A., Iftekharuddin, K., & Tennant, C. (2024). Accelerating cavity fault prediction using deep learning at Jefferson Laboratory. Machine Learning: Science and Technology, 5(3), 1-13, Article 035078. https://doi.org/10.1088/2632-2153/ad7ad6

ORCID

0000-0003-1794-916X (Rahman), 0000-0001-8316-4163 (Iftekharuddin)

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