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RIS citation export for TUXXPLM2: SRF Cavity Fault Classification Using Machine Learning at CEBAF

TY  - CONF
AU  - Solopova, A.D.
AU  - Carpenter, A.
AU  - Iftekharuddin, K.M.
AU  - Powers, T.
AU  - Roblin, Y.
AU  - Tennant, C.
AU  - Vidyaratne, L.
ED  - Boland, Mark
ED  - Tanaka, Hitoshi
ED  - Button, David
ED  - Dowd, Rohan
ED  - Schaa, Volker RW
ED  - Tan, Eugene
TI  - SRF Cavity Fault Classification Using Machine Learning at CEBAF
J2  - Proc. of IPAC2019, Melbourne, Australia, 19-24 May 2019
CY  - Melbourne, Australia
T2  - International Particle Accelerator Conference
T3  - 10
LA  - english
AB  - The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator which makes use of SRF accelerating structures configured in two antiparallel linacs. Each linac consists of twenty C20/C50 cryomodules each containing eight 5-cell cavities and five C100 upgrade cryomodules each containing eight 7-cell cavities. Accurately classifying the source of cavity faults is critical for improving accelerator performance. In addition to archived signals sampled at 10 Hz, a cavity fault triggers a waveform acquisition process where 16 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the effected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. Significant time is required by a subject matter expert to analyze and identify the intra-cavity signatures of imminent faults. This paper describes a path forward that utilizes machine learning for automatic fault classification. Post-training identification of the physical origins of faults are discussed, as are potential machine-trained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 1167
EP  - 1170
KW  - cavity
KW  - cryomodule
KW  - SRF
KW  - operation
KW  - GUI
DA  - 2019/06
PY  - 2019
SN  - 978-3-95450-208-0
DO  - DOI: 10.18429/JACoW-IPAC2019-TUXXPLM2
UR  - http://jacow.org/ipac2019/papers/tuxxplm2.pdf
ER  -