NextGeneration Archiver for WinCC OA

Project goal

Our aim is to make control systems used for the LHC more efficient and smarter. We are working to enhance the functionality of WinCC OA (a SCADA tool used widely at CERN) and to apply data-analytics techniques to the recorded monitoring data, in order to detect anomalies and systematic issues that may impact upon system operation and maintenance.

R&D topic
Machine learning and data analytics
Project coordinator(s)
Fernando Varela
Team members
Filippo Tilaro, Jakub Guzik, Anthony Hennessey, Rafal Kulaga, Piotr Golonka, Peter Sollander, Fernando Varela, Marc Bengulescu, Filip Siroky
Collaborator liaison(s)
Thomas Hahn, Juergen Kazmeier, Alexey Fishkin, Tatiana Mangels, Mikhail Kalinkin, Elisabeth Bakany, Ewald Sperrer

Collaborators

Project background

The HL-LHC programme aims to increase the integrated luminosity — and hence the rate of particle collisions — by a factor of ten beyond the LHC’s design value. Monitoring and control systems will therefore become increasingly complex, with unprecedented data throughputs. Consequently, it is vital to further improve the performance of these systems, and to make use of data-analytics algorithms to detect anomalies and anticipate future behaviour. Achieving this involves a number of related lines of work. This project focuses on the development of a modular and future-proof archiving system (NextGen Archiver) that supports different SQL and NOSQL technologies to enable data analytics. It is important that this can be scaled up to meet our requirements beyond 2020.

Recent progress

Two important milestones for the NextGeneration Archiver (NGA) project were achieved in 2019: preparation of a release for all ETM customers with WinCC OA 3.17 and start of deployment at the ALICE experiment.

Significant progress has been made with all areas of the NGA project, including providing support for redundancy, for complex queries, and for handling signal metadata. In order to improve the performance and scalability of queries, and to make sure that they do not negatively affect core components of the system, direct query functionality was also developed and tested.

In order to ensure reliability of the NGA in large systems with high throughput, several tests were performed at CERN. Existing test automation tools have been significantly extended in order to allow for better synchronisation of testing efforts at CERN and ETM.

Initial results from InfluxDB performance tests performed at CERN show that the technology will most likely not be able to replace the current Oracle technology used for systems with very large numbers of signals (in the range of hundreds of thousands). However, it could successfully act as a shorter-term storage, improving the performance of certain queries and enabling users to easily create web dashboards using Grafana.

Next steps

In 2020, work on the project will continue on many fronts. Increasing test coverage, especially for ‘corner cases’ and failure scenarios, remains one of the main priorities. Work on missing features will continue for all components of the NGA. Further tests of InfluxDB and Apache Kudu will help to determine their performance in large systems. The team will also provide support for ALICE as the experiment prepares to restart after the current long shutdown.

Publications

    P. Golonka, F. Varela-Rodriguez, Consolidation and Redesign of CERN Industrial Controls Frameworks, Proc. 17th Biennial International Conference on Accelerator and Large Experimental Physics Control Systems, New York, 2019. http://cern.ch/go/8RRL

Presentations

    F. M. Tilaro, R. Kulaga, Siemens Data Analytics and SCADA evolution status report (23 January). Presented at CERN openlab Technical Workshop, Geneva, 2019. http://cern.ch/go/kt7K