IPAC 2019, Melbourne, Australia, May 19-24, 2019最新文献

{"title":"SRF Cavity Fault Classification Using Machine Learning At CEBAF","authors":"A. Solopova, A. Carpenter, T. Powers, Y. Roblin, C. Tennant, L. Vidyaratne, K. Iftekharuddin","doi":"10.2172/1981326","DOIUrl":"https://doi.org/10.2172/1981326","url":null,"abstract":"The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator to make use of SRF accelerating structures. The structures are configured in two antiparallel linacs connected by arcs. 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. A cavity fault triggers a waveform acquisition process where 17 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the affected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. This data combined with archived signals sampled at 10 Hz are used to classify faults. Significant time is required for 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 machinetrained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance. DEFINITION OF THE PROBLEM The 12 GeV Upgrade for CEBAF was completed in September 2017. The project doubled the beam energy of the existing accelerator. To meet this energy goal, eleven new 100 MV cryomodules (called C100s) and RF systems were installed in 2013 (see Fig. 1) [1]. Currently the largest contributor to CEBAF downtime are beam trips caused by SRF cavities. During the last year there were an average of 6 RF trips an hour, accounting to roughly 15% of lost beam time per hour every day. To reduce the trip rate accelerating gradient of the cavity needs to be lowered, which means energy reach of CEBAF suffers. The cavities in a C100 cryomodule have strong cavity to cavity mechanical coupling. When one cavity trips off, the Lorentz force detuning causes vibrations in the cavity string that are sufficient to trip other cavities. In order to avoid trips, the entire string is switched to self-excited loop mode (frequency tracking) when one of the cavities trips and others become unstable. This is also the default response for various other off normal conditions, which makes it difficult to determine which cavity initiated the cascade of faults [2]. When a cavities trips off, it disrupts delivery of the beam to the experimental halls. Correctly classifying which of several known fault mechanisms caused the cavity to trip provides valuable information to control room operators on how to treat the offending cavity and ultimately helps to maintain greater beam availability to users [3]. Figure 1: Schematic of the CEBAF accelerator showing the locations of the","PeriodicalId":212195,"journal":{"name":"IPAC 2019, Melbourne, Australia, May 19-24, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133554324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}