Author: Baker, K.R.L.
Paper Title Page
MOPV019 PVEcho: Design of a Vista/EPICS Bridge for the ISIS Control System Transition 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  
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
FRBL01 Machine Learning for Anomaly Detection in Continuous Signals 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)