Keyword: data-acquisition
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TUPAB291 Subsystem Level Data Acquisition for the Optical Synchronization System at European XFEL FEL, controls, laser, database 2167
 
  • M. Schütte, A. Eichler, T. Lamb, V. Rybnikov, H. Schlarb, T. Wilksen
    DESY, Hamburg, Germany
  
  The op­ti­cal syn­chro­niza­tion sys­tem for the Eu­ro­pean X-Ray Free-Elec­tron Laser pro­vides sub-10 fem­tosec­ond tim­ing pre­ci­sion * for the ac­cel­er­a­tor sub­sys­tems and ex­per­i­ments. This is achieved by phase lock­ing a mode-locked laser os­cil­la­tor to the main RF ref­er­ence and dis­trib­ut­ing the op­ti­cal pulse train car­ry­ing the time in­for­ma­tion via ac­tively prop­a­ga­tion-time sta­bi­lized op­ti­cal fibers to mul­ti­ple end-sta­tions. Mak­ing up roughly one per­cent of the en­tire Eu­ro­pean XFEL, it is the first sub­sys­tem to re­ceive a large-scale data ac­qui­si­tion sys­tem [2] for stor­ing not just hand-se­lected in­for­ma­tion, but in fact all di­ag­nos­tic, mon­i­tor­ing, and con­fig­u­ra­tion data rel­e­vant to the op­ti­cal syn­chro­niza­tion avail­able from the dis­trib­uted con­trol sys­tem in­fra­struc­ture. A min­i­mum of 100 TB per year may be stored in a per­sis­tent archive for long-term health mon­i­tor­ing and data min­ing whereas ex­cess data is stored in a short-term ring buffer for high-res­o­lu­tion fault analy­sis and fea­ture ex­trac­tion al­go­rithm de­vel­op­ment. This paper de­scribes scale, chal­lenges and first ex­pe­ri­ences from the op­ti­cal syn­chro­niza­tion data ac­qui­si­tion sys­tem.
* S. Schulz et al., "Few-Femtosecond Facility-Wide Sync. of the European XFEL," in Proc. FEL’19
** T. Wilksen et al., "A Bunch-Sync. DAQ System for the European XFEL," in Proc. ICALEPCS’17 		 
poster icon Poster TUPAB291 [0.281 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB291  
About • paper received ※ 14 May 2021       paper accepted ※ 17 June 2021       issue date ※ 24 August 2021  
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TUPAB295 Upgrade to the EPICS Control System at the Argonne Wakefield Accelerator Test Facility controls, EPICS, interface, LLRF 2173
 
  • W. Liu, J.M. Byrd, D.S. Doran, G. Ha, A.N. Johnson, P. Piot, J.G. Power, J.H. Shao, G. Shen, C. Whiteford, E.E. Wisniewski
    ANL, Lemont, Illinois, USA
  
  Funding: US Department of Energy, Office of Science
The Ar­gonne Wake­field Ac­cel­er­a­tor (AWA) Test Fa­cil­ity has used a com­pletely home­brewed, MS Win­dows-based con­trol sys­tem for the last 20 years. In an ef­fort to mod­ern­ize the con­trol sys­tem and pre­pare for an ac­tive ma­chine learn­ing pro­gram, the AWA will work with the Ad­vanced Pho­ton Source (APS) con­trols group to up­grade its con­trol sys­tem to EPICS. The EPICS con­trol sys­tem is ex­pected to fa­cil­i­tate col­lab­o­ra­tions and sup­port the fu­ture growth of AWA. An overview of the pre­vi­ous AWA con­trol and data ac­qui­si­tion sys­tem is pre­sented, along with a vi­sion and path for com­plet­ing the EPICS up­grade. 		 
poster icon Poster TUPAB295 [1.108 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB295  
About • paper received ※ 19 May 2021       paper accepted ※ 01 July 2021       issue date ※ 30 August 2021  
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WEPAB323 High Performance DAQ Infrastructure to Enable Machine Learning for the Advanced Photon Source Upgrade monitoring, controls, EPICS, hardware 3434
 
  • G. Shen, N.D. Arnold, T.G. Berenc, J. Carwardine, E. Chandler, T. Fors, T.J. Madden, D.R. Paskvan, C. Roehrig, S.E. Shoaf, S. Veseli
    ANL, Lemont, Illinois, USA
  
  Funding: Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract DE-AC02-06CH11357.
It is well known that the ef­fi­ciency of an ad­vanced con­trol al­go­rithm like ma­chine learn­ing is as good as its data qual­ity. Much re­cent progress in tech­nol­ogy en­ables the mas­sive data ac­qui­si­tion from a con­trol sys­tem of mod­ern par­ti­cle ac­cel­er­a­tor, and the wide use of em­bed­ded con­trollers, like field-pro­gram­ma­ble gate ar­rays (FPGA), pro­vides an op­por­tu­nity to col­lect fast data from tech­ni­cal sub­sys­tems for mon­i­tor­ing, sta­tis­tics, di­ag­nos­tics or fault record­ing. To im­prove the data qual­ity, at the APS Up­grade pro­ject, a gen­eral-pur­pose data ac­qui­si­tion (DAQ) sys­tem is under ac­tive de­vel­op­ment. The APS-U DAQ sys­tem col­lects high-qual­ity fast data from un­der­neath em­bed­ded con­trollers, es­pe­cially the FPGAs, with the man­ner of time-cor­re­la­tion and syn­chro­nously sam­pling, which could be used for com­mis­sion­ing, per­for­mance mon­i­tor­ing, trou­bleshoot­ing, and early fault de­tec­tion, etc. This paper pre­sents the de­sign and lat­est progress of APS-U high-per­for­mance DAQ in­fra­struc­ture, as well as its prepa­ra­tion to en­able the use of ma­chine learn­ing tech­nol­ogy for APS-U, and its use cases at APS. 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB323  
About • paper received ※ 19 May 2021       paper accepted ※ 24 June 2021       issue date ※ 29 August 2021  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)