Publications
Publication Information
| Title | Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory |
| Authors | Md Monibor Rahman, Adam Carpenter, Khan Iftekharuddin, Christopher Tennant |
| JLAB number | JLAB-ACP-24-4013 |
| LANL number | (None) |
| Other number | DOE/OR/23177-7442 |
| Document Type(s) | (Journal Article) |
| Category: | Computational Physics |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Journal Compiled for Machine Learning: Science and Technology Volume 5 Issue 035078 Refereed | |
| Publication 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 LSTM-CNN 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. |
| Experiment Numbers: | |
| Group: | Ctr for Adv Stud of Accel |
| Document: | docx |
| DOI: | https://doi.org/10.1088/2632-2153/ad7ad6 |
| Accepted Manuscript: | Rahman_2024_Mach._Learn.__Sci._Technol._5_035078.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |