论文标题
杰斐逊实验室使用机器学习的超导射频腔故障分类
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory
论文作者
论文摘要
我们报告了用于对杰斐逊实验室连续电子束加速器设施（CEBAF）中C100超导射频（SRF）腔断层进行分类的机器学习模型的开发。 CEBAF是一种使用418个SRF腔的连续波再循环的Linac，通过5个频率加速了高达12 GEV的电子。其中，设计了96个腔（12个冷冻模块），采用配置的数字低级RF系统设计，使得腔断层触发了冷冻模块中8个腔的17 RF信号的波形记录。主题专家（SME）能够分析收集的时间序列数据，并确定八个腔体中的哪一个首先出现故障并对故障类型进行分类。该信息用于查找趋势并从战略上部署缓解措施，以解决有问题的冷冻模块。但是，手动标记数据是费力且耗时的。通过利用机器学习，已经实施了对故障腔和故障类型的分类的接近实时（而不是验尸）的识别。我们在最近的物理运行中讨论了ML模型的性能。结果表明，空腔识别和断层分类模型的精度分别为84.9％和78.2％。
We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the 8 cavities in the cryomodule. Subject matter experts (SME) are able to analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time (rather than post-mortem) identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the ML models during a recent physics run. Results show the cavity identification and fault classification models have accuracies of 84.9% and 78.2%, respectively.
