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-timerather than postmortemidentification 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.
Modern molecular electronic transfer (MET) angular motion sensors combine high technical characteristics with low cost. Self-noise is one of the key characteristics which determine applications for MET sensors. However, until the present there has not been a model describing the sensor noise in the complete operating frequency range. The present work reports the results of an experimental study of the self-noise level of such sensors in the frequency range of 0.01–200 Hz. Based on the experimental data, a theoretical model is developed. According to the model, self-noise is conditioned by thermal hydrodynamic fluctuations of the operating fluid flow in the frequency range of 0.01–2 Hz. At the frequency range of 2–100 Hz, the noise power spectral density has a specific inversely proportional dependence of the power spectral density on the frequency that could be attributed to convective processes. In the high frequency range of 100–200 Hz, the noise is conditioned by the voltage noise of the electronics module input stage operational amplifiers and is heavily reliant to the sensor electrical impedance. The presented results allow a deeper understanding of the molecular electronic sensor noise nature to suggest the ways to reduce it.
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