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RIS citation export for TUCPA04: Model Learning Algorithms for Anomaly Detection in CERN Control Systems

AU - Tilaro, F.M.
AU - Bradu, B.
AU - Gonzalez-Berges, M.
AU - Roshchin, M.
AU - Varela, F.
ED - Schaa, Volker RW
ED - Costa, Isidre
ED - Fernández, David
ED - Matilla, Óscar
TI - Model Learning Algorithms for Anomaly Detection in CERN Control Systems
J2 - Proc. of ICALEPCS2017, Barcelona, Spain, 8-13 October 2017
C1 - Barcelona, Spain
T2 - International Conference on Accelerator and Large Experimental Control Systems
T3 - 16
LA - english
AB - At CERN there are over 600 different industrial control systems with millions of deployed sensors and actuators and their monitoring represents a challenging and complex task. This paper describes three different mathematical approaches that have been designed and developed to detect anomalies in CERN control systems. Specifically, one of these algorithms is purely based on expert knowledge while the other two mine historical data to create a simple model of the system, which is then used to detect anomalies. The methods presented can be categorized as dynamic unsupervised anomaly detection; "dynamic" since the behaviour of the system is changing in time, "unsupervised" because they predict faults without reference to prior events. Consistent deviations from the historical evolution can be seen as warning signs of a possible future anomaly that system experts or operators need to check. The paper also presents some results, obtained from the analysis of the LHC Cryogenic system. Finally the paper briefly describes the deployment of Spark and Hadoop into the CERN environment to deal with huge datasets and to spread the computational load of the analysis across multiple nodes.
CP - Geneva, Switzerland
SP - 265
EP - 271
KW - ion
KW - controls
KW - cryogenics
KW - operation
KW - monitoring
DA - 2018/01
PY - 2018
SN - 978-3-95450-193-9
DO - 10.18429/JACoW-ICALEPCS2017-TUCPA04
UR - http://jacow.org/icalepcs2017/papers/tucpa04.pdf
ER -