ICALEPCS2017 - List of Authors (Roshchin, M.)

Paper	Title	Page
TUCPA04	Model Learning Algorithms for Anomaly Detection in CERN Control Systems	265
	F.M. Tilaro, B. Bradu, M. Gonzalez-Berges, F. Varela CERN, Geneva, Switzerland M. Roshchin Siemens AG, Corporate Technology, München, Germany
	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.
	Slides TUCPA04 [1.965 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUCPA04
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Model Learning Algorithms for Anomaly Detection in CERN Control Systems

265

F.M. Tilaro, B. Bradu, M. Gonzalez-Berges, F. Varela
CERN, Geneva, Switzerland
M. Roshchin
Siemens AG, Corporate Technology, München, Germany

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.

Slides TUCPA04 [1.965 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUCPA04

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)