JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
TY - CONF AU - Saoulis, A.A. AU - Baker, K.R.L. AU - Burridge, R.A. AU - Lilley, S. AU - Romanovschi, M. ED - Furukawa, Kazuro ED - Yan, Yingbing ED - Leng, Yongbin ED - Chen, Zhichu ED - Schaa, Volker R.W. TI - Machine Learning for Anomaly Detection in Continuous Signals J2 - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021 CY - Shanghai, China T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 18 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 1032 EP - 1038 KW - network KW - operation KW - neutron KW - controls KW - software DA - 2022/03 PY - 2022 SN - 2226-0358 SN - 978-3-95450-221-9 DO - doi:10.18429/JACoW-ICALEPCS2021-FRBL01 UR - https://jacow.org/icalepcs2021/papers/frbl01.pdf ER -