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TY - CONF 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 - FernÃ¡ndez, David ED - Matilla, Ãscar 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. PB - JACoW 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 -