ICALEPCS2017 - List of Authors (Tilaro, F.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)

TUPHA035	Data Analytics Reporting Tool for CERN SCADA Systems	456
	P.J. Seweryn, M. Gonzalez-Berges, B. Schofield, F.M. Tilaro CERN, Geneva, Switzerland
	This paper describes the concept of a generic data analytics reporting tool for SCADA (Supervisory Control and Data Acquisition) systems at CERN. The tool is a response to a growing demand for smart solutions in the supervision and analysis of control systems data. Large scale data analytics is a rapidly advancing field, but simply performing the analysis is not enough; the results must be made available to the appropriate users (for example operators and process engineers). The tool can report data analytics for objects such as valves and PID controllers directly into the SCADA systems used for operations. More complex analyses involving process interconnections (such as correlation analysis based on machine learning) can also be displayed. A pilot project is being developed for the WinCC Open Architecture (WinCC OA) SCADA system using Hadoop for storage. The reporting tool obtains the metadata and analysis results from Hadoop using Impala, but can easily be switched to any database system that supports SQL standards.
	Poster TUPHA035 [1.016 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUPHA035
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEAPL02	Automatic PID Performance Monitoring Applied to LHC Cryogenics	1017
	B. Bradu, E. Blanco Viñuela, R. Marti, F.M. Tilaro CERN, Geneva, Switzerland
	At CERN, the LHC (Large Hadron Collider) cryogenic system employs about 4900 PID (Proportional Integral Derivative) regulation loops distributed over the 27 km of the accelerator. Tuning all these regulation loops is a complex task and the systematic monitoring of them should be done in an automated way to be sure that the overall plant performance is improved by identifying the poorest performing PID controllers. It is nearly impossible to check the performance of a regulation loop with a classical threshold technique as the controlled variables could evolve in large operation ranges and the amount of data cannot be manually checked daily. This paper presents the adaptation and the application of an existing regulation indicator performance algorithm on the LHC cryogenic system and the different results obtained in the past year of operation. This technique is generic for any PID feedback control loop, it does not use any process model and needs only a few tuning parameters. The publication also describes the data analytics architecture and the different tools deployed on the CERN control infrastructure to implement the indicator performance algorithm.
	Talk as video stream: https://youtu.be/7dCglp2Pn_c
	Slides WEAPL02 [1.651 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-WEAPL02
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)

TUPHA035

Data Analytics Reporting Tool for CERN SCADA Systems

456

P.J. Seweryn, M. Gonzalez-Berges, B. Schofield, F.M. Tilaro
CERN, Geneva, Switzerland

This paper describes the concept of a generic data analytics reporting tool for SCADA (Supervisory Control and Data Acquisition) systems at CERN. The tool is a response to a growing demand for smart solutions in the supervision and analysis of control systems data. Large scale data analytics is a rapidly advancing field, but simply performing the analysis is not enough; the results must be made available to the appropriate users (for example operators and process engineers). The tool can report data analytics for objects such as valves and PID controllers directly into the SCADA systems used for operations. More complex analyses involving process interconnections (such as correlation analysis based on machine learning) can also be displayed. A pilot project is being developed for the WinCC Open Architecture (WinCC OA) SCADA system using Hadoop for storage. The reporting tool obtains the metadata and analysis results from Hadoop using Impala, but can easily be switched to any database system that supports SQL standards.

Poster TUPHA035 [1.016 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUPHA035

Export •

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

WEAPL02

Automatic PID Performance Monitoring Applied to LHC Cryogenics

1017

B. Bradu, E. Blanco Viñuela, R. Marti, F.M. Tilaro
CERN, Geneva, Switzerland

At CERN, the LHC (Large Hadron Collider) cryogenic system employs about 4900 PID (Proportional Integral Derivative) regulation loops distributed over the 27 km of the accelerator. Tuning all these regulation loops is a complex task and the systematic monitoring of them should be done in an automated way to be sure that the overall plant performance is improved by identifying the poorest performing PID controllers. It is nearly impossible to check the performance of a regulation loop with a classical threshold technique as the controlled variables could evolve in large operation ranges and the amount of data cannot be manually checked daily. This paper presents the adaptation and the application of an existing regulation indicator performance algorithm on the LHC cryogenic system and the different results obtained in the past year of operation. This technique is generic for any PID feedback control loop, it does not use any process model and needs only a few tuning parameters. The publication also describes the data analytics architecture and the different tools deployed on the CERN control infrastructure to implement the indicator performance algorithm.

Talk as video stream: https://youtu.be/7dCglp2Pn_c

Slides WEAPL02 [1.651 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-WEAPL02

Export •

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