Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory
This work addresses the disruption of beam delivery in particle accelerators, offering a domain-specific solution for operational efficiency, though it is incremental as it applies existing deep learning methods to a new dataset.
The study tackled the problem of predicting slowly developing faults in accelerating cavities at Jefferson Laboratory's CEBAF facility, using a deep learning model that achieved 99.99% accuracy for normal signals and correctly predicted 80% of faults several hundred milliseconds before onset.
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.