IPAC2019 - List of Authors (Solopova, A.D.)

Paper	Title	Page
TUXXPLM2	SRF Cavity Fault Classification Using Machine Learning at CEBAF	1167
	A.D. Solopova, A. Carpenter, T. Powers, Y. Roblin, C. Tennant JLab, Newport News, Virginia, USA K.M. Iftekharuddin, L. Vidyaratne ODU, Norfolk, Virginia, USA
	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
	Slides TUXXPLM2 [4.404 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-TUXXPLM2
About •	paper received ※ 14 May 2019 paper accepted ※ 23 May 2019 issue date ※ 21 June 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

SRF Cavity Fault Classification Using Machine Learning at CEBAF

1167

A.D. Solopova, A. Carpenter, T. Powers, Y. Roblin, C. Tennant
JLab, Newport News, Virginia, USA
K.M. Iftekharuddin, L. Vidyaratne
ODU, Norfolk, Virginia, USA

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

Slides TUXXPLM2 [4.404 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-TUXXPLM2

About •

paper received ※ 14 May 2019 paper accepted ※ 23 May 2019 issue date ※ 21 June 2019

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)