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Related Experiment Videos

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory.

Lasitha Vidyaratne1, Adam Carpenter1, Tom Powers1

  • 1Jefferson Laboratory, Newport News, VA, United States.

Frontiers in Artificial Intelligence
|January 20, 2022
PubMed
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Deep learning models show promise for classifying superconducting radio-frequency cavity faults, offering faster inference speeds for accelerator operations. While comparable in cavity identification, they slightly trail traditional methods in fault classification accuracy.

Area of Science:

  • Accelerator Physics
  • Machine Learning Applications
  • Superconducting Radio-Frequency (SRF) Technology

Background:

  • The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab uses 418 SRF cavities for high-energy electron acceleration.
  • Automating the analysis of RF time-series data from cavity failures is crucial due to the large data volume and manual inspection limitations.
  • Previous work utilized traditional machine learning (ML) for fault classification, motivating exploration of deep learning (DL) for enhanced performance.

Purpose of the Study:

  • To investigate the efficacy of deep learning (DL) models, specifically recurrent neural networks (RNNs) and convolutional neural networks (CNNs), for classifying SRF cavity faults.
  • To develop a DL system capable of fast inference for potential real-time fault prediction and intervention.
  • To compare the performance of DL models against existing state-of-the-art ML fault classification models.
Keywords:
LINACconvolutional neural networksdeep recurrent learningfault identificationparticle acceleratorsuperconducting radio-frequency cavitiestime-series classification

Related Experiment Videos

Main Methods:

  • Utilized a dataset of RF waveform data from past operational runs of CEBAF.
  • Implemented and analyzed deep recurrent neural networks (RNNs), including those with long short-term memory (LSTM) layers.
  • Implemented and analyzed deep convolutional neural networks (CNNs).
  • Compared the performance of RNN and CNN models against a traditional ML fault classification model.

Main Results:

  • Deep learning architectures achieved performance comparable to state-of-the-art ML models for cavity identification.
  • DL models demonstrated slightly lower accuracy in fault classification compared to traditional ML methods.
  • A significant advantage of DL models was their faster inference speed, crucial for predictive applications.

Conclusions:

  • Deep learning approaches, including RNNs and CNNs, are effective for classifying SRF cavity faults in accelerator facilities.
  • The faster inference times of DL models offer potential for real-time fault detection and mitigation strategies.
  • Further research may focus on improving DL fault classification accuracy while retaining inference speed benefits.