ICALEPCS2017 - List of Authors (Schofield, B.)

Paper	Title	Page
TUPHA034	SCADA Statistics Monitoring Using the Elastic Stack (Elasticsearch, Logstash, Kibana)	451
	J.A.G. Hamilton, M. Gonzalez-Berges, B. Schofield, J-C. Tournier CERN, Geneva, Switzerland
	The Industrial Controls and Safety systems group at CERN, in collaboration with other groups, has developed and currently maintains around 200 controls applications that include domains such as LHC magnet protection, cryogenics and electrical network supervision systems. Millions of value changes and alarms from many devices are archived to a centralised Oracle database but it is not easy to obtain high-level statistics from such an archive. A system based on the Elastic Stack has been implemented in order to provide easy access to these statistics. This system provides aggregated statistics based on the number of value changes and alarms, classified according to several criteria such as time, application domain, system and device. The system can be used, for example, to detect abnormal situations and alarm misconfiguration. In addition to these statistics each application generates text-based log files which are parsed, collected and displayed using the Elastic Stack to provide centralised access to all the application logs. Further work will explore the possibilities of combining the statistics and logs to better understand the behaviour of CERN's controls applications.
	Poster TUPHA034 [5.094 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUPHA034
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)

Paper

Title

Page

SCADA Statistics Monitoring Using the Elastic Stack (Elasticsearch, Logstash, Kibana)

451

J.A.G. Hamilton, M. Gonzalez-Berges, B. Schofield, J-C. Tournier
CERN, Geneva, Switzerland

The Industrial Controls and Safety systems group at CERN, in collaboration with other groups, has developed and currently maintains around 200 controls applications that include domains such as LHC magnet protection, cryogenics and electrical network supervision systems. Millions of value changes and alarms from many devices are archived to a centralised Oracle database but it is not easy to obtain high-level statistics from such an archive. A system based on the Elastic Stack has been implemented in order to provide easy access to these statistics. This system provides aggregated statistics based on the number of value changes and alarms, classified according to several criteria such as time, application domain, system and device. The system can be used, for example, to detect abnormal situations and alarm misconfiguration. In addition to these statistics each application generates text-based log files which are parsed, collected and displayed using the Elastic Stack to provide centralised access to all the application logs. Further work will explore the possibilities of combining the statistics and logs to better understand the behaviour of CERN's controls applications.

Poster TUPHA034 [5.094 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUPHA034

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)