Keyword: neutron
Paper Title Other Keywords Page
MOPV019 PVEcho: Design of a Vista/EPICS Bridge for the ISIS Control System Transition EPICS, controls, hardware, software 164
 
  • K.R.L. Baker, I.D. Finch, G.D. Howells, M. Romanovschi, A.A. Saoulis
    STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
 
  Funding: UKRI / STFC
The migration of the ISIS Controls System from Vsystem to EPICS presents a significant challenge and risk to the day-to-day operations of the accelerator. An evaluation of potential options has indicated that the most effective migration method to mitigate against this risk is to make use of a ‘hybrid’ system running Vsystem and EPICS simultaneously. This allows for a phased porting of controls hardware from the existing software to EPICS. This work will outline the prototype Vsystem/EPICS bridge that will facilitate this hybrid operation, referred to as pvecho. The bridge has been developed in Python, utilising existing communication from Vsystem to an MQTT broker developed as part of a previous project. Docker containers have been used for its development to create an isolated test environment to allow the software to communicate with other services currently used at ISIS.
 
poster icon Poster MOPV019 [1.528 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOPV019  
About • Received ※ 08 October 2021       Accepted ※ 04 November 2021       Issue date ※ 08 January 2022  
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TUPV049 The IBEX Script Generator experiment, controls, software, EPICS 519
 
  • J.C. King, J.R. Harper, A.J. Long, T. Löhnert, D.E. Oram
    STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
 
  Experiment scripting is a key element of maximising utilisation of beam time at the ISIS Neutron and Muon Source, but can be prone to typing and logic errors. The IBEX Script Generator enables collaboration between instrument users and scientists to remove the need to write a script for many experiments, so improving reliability and control. For maximum applicability, the script generator needs to be easily configurable. Instrument scientists define action parameters, and functions for action execution, time estimation and validation, to produce a "script definition". A user then generates a Python script by organising a table of actions and their values, which are validated in real time, and can then be submitted to a script server for execution. Py4J is used to bridge a Java front end with Python script definitions. An iterative user-focused approach has been employed with Squish UI testing to achieve a behaviour-driven development workflow, along with Jenkins for continuous integration. Further planned development includes dynamic scripting ’ controlling the execution of actions during the experiment ’ action iteration and user experience improvement.  
poster icon Poster TUPV049 [1.051 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-TUPV049  
About • Received ※ 09 October 2021       Revised ※ 19 October 2021       Accepted ※ 20 November 2021       Issue date ※ 23 November 2021
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WEPV022 Sample Alignment in Neutron Scattering Experiments Using Deep Neural Network network, experiment, alignment, scattering 686
 
  • J.P. Edelen, K. Bruhwiler, A. Diaw, C.C. Hall
    RadiaSoft LLC, Boulder, Colorado, USA
  • S. Calder
    ORNL RAD, Oak Ridge, Tennessee, USA
  • C.M. Hoffmann
    ORNL, Oak Ridge, Tennessee, USA
 
  Funding: DOE Office of Science Office of Basic Energy Science SBIR award number DE-SC0021555
Access to neutron scattering centers, such as Oak Ridge National Laboratory (ORNL) and the NIST Center for Neutron Research, has provided beam energies to investigating a wide variety of applications such as particle physics, material science, and biology. In these experiments, the quality of collected data is very sensitive to sample and beam alignment, and stabilization of the experimental environment, requiring human intervention to tune the beam. While this procedure works, it is inefficient and time-consuming. In the work we present progress towards using machine learning to automate the alignment of a beamline in neutron scattering experiments. Our algorithm uses convolutional neural network to both learn a surrogate of the image data of the sample and to predict the sample contour using a u-net. We tested our algorithm on neutron camera images from the H2-BA powder diffractometer and the Topaz single crystal diffractometer beamlines of ORNL.
 
poster icon Poster WEPV022 [4.472 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV022  
About • Received ※ 10 October 2021       Revised ※ 22 October 2021       Accepted ※ 21 December 2021       Issue date ※ 06 February 2022
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WEPV041 Implementation of a VHDL Application for Interfacing Anybus CompactCom interface, network, FPGA, PLC 755
 
  • S. Gabourin, A. Nordt, S. Pavinato
    ESS, Lund, Sweden
 
  The European Spallation Source (ESS ERIC), based in Lund (Sweden), will be in a few years the most powerful neutron source in Europe with an average beam power of 5 MW. It will accelerate proton beam pulses to a Tungsten wheel to generate neutrons by the spallation effect. For such beam, the Machine Protection System (MPS) at ESS must be fast and reliable, and for this reason a Fast Beam Interlock System (FBIS) based on FPGAs is required. Some protection functions monitoring slow values (like temperature, mechanical movements, magnetic fields) need however less strict reaction times and are managed by PLCs. The communications protocol established between PLCs and FBIS is PROFINET fieldbus based. The Anybus CompactCom allows an host to have connectivity to industrial networks as PROFINET. In this context, FBIS represents the host and the application code to interface the AnyBus CompactCom has been fully developed in VHDL. This paper describes an open source implementation to interface a CompactCom M40 with an FPGA.  
poster icon Poster WEPV041 [0.967 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV041  
About • Received ※ 09 October 2021       Revised ※ 22 October 2021       Accepted ※ 14 January 2022       Issue date ※ 01 March 2022
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WEPV049 Controls Data Archiving at the ISIS Neutron and Muon Source for In-Depth Analysis and ML Applications EPICS, controls, software, database 780
 
  • I.D. Finch, G.D. Howells, A.A. Saoulis
    STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
 
  Funding: UKRI / STFC
The ISIS Neutron and Muon Source accelerators are currently operated using Vsystem control software. Archiving of controls data is necessary for immediate fault finding, to facilitate analysis of long-term trends, and to provide training datasets for machine learning applications. While Vsystem has built-in logging and data archiving tools, in recent years we have greatly expanded the range and quantity of data archived using an open-source software stack including MQTT as a messaging system, Telegraf as a metrics collection agent, and the Influx time-series database as a storage backend. Now that ISIS has begun the transition from Vsystem to EPICS this software stack will need to be replaced or adapted. To explore the practicality of adaptation, a new Telegraf plugin allowing direct collection of EPICS data has been developed. We describe the current Vsystem-based controls data archiving solution in use at ISIS, future plans for EPICS, and our plans for the transition while maintaining continuity of data.
 
poster icon Poster WEPV049 [0.845 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV049  
About • Received ※ 09 October 2021       Revised ※ 19 October 2021       Accepted ※ 22 December 2021       Issue date ※ 19 January 2022
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FRBL01 Machine Learning for Anomaly Detection in Continuous Signals network, operation, controls, software 1032
 
  • A.A. Saoulis, K.R.L. Baker, R.A. Burridge, S. Lilley, M. Romanovschi
    STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
 
  Funding: UKRI / STFC
High availability at accelerators such as the ISIS Neutron and Muon Source is a key operational goal, requiring rapid detection and response to anomalies within the accelerator’s subsystems. While monitoring systems are in place for this purpose, they often require human expertise and intervention to operate effectively or are limited to predefined classes of anomaly. Machine learning (ML) has emerged as a valuable tool for automated anomaly detection in time series signal data. An ML pipeline suitable for anomaly detection in continuous signals is described, from labeling data for supervised ML algorithms to model selection and evaluation. These techniques are applied to detecting periods of temperature instability in the liquid methane moderator on ISIS Target Station 1. We demonstrate how this ML pipeline can be used to improve the speed and accuracy of detection of these anomalies.
 
slides icon Slides FRBL01 [12.611 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-FRBL01  
About • Received ※ 08 October 2021       Revised ※ 27 October 2021       Accepted ※ 21 December 2021       Issue date ※ 24 January 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)