Keyword: EPICS
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TUPA47 Upgrade from ADCs with Centrally Scheduled Triggers to Continually Triggered Waveform Digitizers controls, FPGA, experiment, timing 452
  • S.A. Baily, B.C. Atencio, A.J. Braido, C.D. Hatch, J.O. Hill, S.M. Johnson, L.S. Kennel, M. Pieck, L.E. Walker, H.A. Watkins, E.E. Westbrook, K. Xu, D.D. Zimmermann
    LANL, Los Alamos, New Mexico, USA
  The Los Alamos Neutron Science Center (LANSCE) control system includes many data channels that are timed and flavored, i.e., users can specify the species of beam and time within the beam pulse at which data are reported. The legacy LANSCE control system accom-plished this task by queuing up application software-initiated requests and scheduling Analog to Digital Con-verter (ADC) readout with custom programmable time-delay gated and multiplexed Remote Information and Control Equipment (RICE). This year we upgraded this system to a new Experimental Physics and Industrial Control System (EPICS) system that includes signal ded-icated waveform digitizer. An appropriate subset of the data is then returned as specified by each client. This is made possible by improvements to EPICS software, a Commercial Off-The-Shelf (COTS) Field Programmable Gate Array (FPGA) Mezzanine Card (FMC) based ADC and a COTS VPX FPGA card with EPICS embedded on a soft-core processor. This year we upgraded over 1200 waveform channels from RICE to the new TDAQ (Timed/flavored Data Acquisition) system.
poster icon Poster TUPA47 [1.379 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA47  
About • Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 05 October 2022
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TUPA62 LANSCE Control System’s 50th Anniversary controls, timing, network, data-acquisition 482
  • M. Pieck, C.D. Hatch, J.O. Hill, H.A. Watkins, E.E. Westbrook
    LANL, Los Alamos, New Mexico, USA
  After almost exactly 50 years in service, the LANSCE (Los Alamos Neutron Science Center) control system has achieved a major milestone, replacing its original and reliable RICE (Remote Instrumentation and Control Equipment) with a modern customized control system. The task of replacing RICE was challenging because of its technology (late 1960’s), number of channels (>10,000), unique characteristics (all-modules data takes, timed/flavored data takes) and that it was designed as an integral part of the whole accelerator. We discuss the history, RICE integral architecture, upgrade efforts, and the new system providing cutting-edge capabilities. The boundary condition was that upgrades only could be implemented during the annual four-month accelerator maintenance outage. This led to a multi-phased project which turned out to be about an 11-year effort.  
poster icon Poster TUPA62 [1.985 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA62  
About • Received ※ 02 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 25 September 2022
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WEPA23 SRF Cavity Instability Detection with Machine Learning at CEBAF cavity, SRF, linac, controls 669
  • D.L. Turner, R. Bachimanchi, A. Carpenter, J. Latshaw, C. Tennant, L.S. Vidyaratne
    JLab, Newport News, Virginia, USA
  Funding: Authored by Jefferson Science Associates, LLC under U.S. DOE Contract No. DE-AC05-06OR23177.
During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.
poster icon Poster WEPA23 [1.859 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA23  
About • Received ※ 01 August 2022 — Revised ※ 04 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 24 August 2022
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