Author: Burridge, R.A.
Paper Title Page
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.
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About • Received ※ 08 October 2021       Revised ※ 27 October 2021       Accepted ※ 21 December 2021       Issue date ※ 24 January 2022
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