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RIS citation export for WEPV023: Development of a Smart Alarm System for the CEBAF Injector

AU  - Abell, D.T.
AU  - Edelen, J.P.
AU  - Freeman, B.G.
AU  - Kazimi, R.
AU  - Moser, D.G.
AU  - Tennant, C.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - Development of a Smart Alarm System for the CEBAF Injector
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  - RadiaSoft and Jefferson Laboratory are working together to develop a machine-learning-based smart alarm system for the CEBAF injector. Because of the injector’s large number of parameters and possible fault scenarios, it is highly desirable to have an autonomous alarm system that can quickly identify and diagnose unusual machine states. We present our work on artificial neural networks designed to identify such undesirable machine states. In particular, we test both auto-encoders and inverse models as possible tools for differentiating between normal and abnormal states. These models are being developed using both supervised and unsupervised learning techniques, and are being trained using CEBAF injector data collected during dedicated machine studies as well as during regular operations. Lastly, we discuss tradeoffs between the two types of models.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 691
EP  - 694
KW  - operation
KW  - network
KW  - vacuum
KW  - solenoid
KW  - quadrupole
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-WEPV023
UR  - https://jacow.org/icalepcs2021/papers/wepv023.pdf
ER  -