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RIS citation export for FRBL01: Machine Learning for Anomaly Detection in Continuous Signals

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  -