Data analytics for industrial controls and monitoring 

Project goal

This project is working to render the industrial control systems used for the LHC more efficient and more intelligent. The aim is to develop a data-analytics platform that capitalises on the latest advances in artificial intelligence (AI), cloud and edge-computing technologies. The ultimate goal is to make use of analytics solutions provided by Siemens to provide non-expert end users with a turnkey data-analytics service.

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


Project background

The HL-LHC project 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 to anticipate future behaviour. Achieving this involves a number of related lines of work. This particular project focuses on the development of a data-analytics platform that combines the benefits of cloud and edge computing.

Recent progress

In the first half of 2019, we focused on the monitoring of various LHC control systems, using two distinct analytics solutions from Siemens: Smart IIoT, a framework used to monitor a multitude of control signals in a distributed manner, and ELVis, a web-based platform for handling multiple streams of time-series data from sensors. Achieving tighter integration between ELVis and Smart IIoT was one of the main objectives for the first half of 2019. A single interface was developed to enable users to define complex event-processing rules, configure the cloud and edge infrastructure, and monitor the execution of the analyses.

In the second half of 2019, Filip Siroky, a new fellow funded by CERN openlab, joined the team. His work has focused on the following: optimising the ion-beam source for the LINAC3 accelerator at CERN; deploying Siemens’s Distributed Complex Event Processing (DCEP) technology to enable advanced data analytics and predictive maintenance for the oxygen-deficiency sensors in the LHC tunnel; and integrating an array of Siemens IoT infrared sensors for detecting room occupancy into the central room booking system at CERN.

Next steps

One of the main objectives for 2020 is to integrate the DCEP technology with the control systems of other equipment groups at CERN: cryogenics, electricity, and cooling and ventilation. The other aim is to provide a service for collection of generic AI algorithms that could easily be employed by people who are not data scientists, helping them to perform advanced analytics on controls data.




    F. Tilaro, F. Varela, Model Learning Algorithms for Anomaly Detection in CERN Control Systems (25 January). Presented at BE-CO Technical Meeting, Geneva, 2018.
    F. Tilaro, F. Varela, Industrial IoT in CERN Control Systems (21 February). Presented at Siemens IoT Conference, Nuremberg, 2018.
    F. Tilaro, F. Varela, Optimising CERN control systems through Anomaly Detection & Machine Learning (29 August). Presented at AI workshop for Future Production Systems, Lund, 2018.
    F. Tilaro, F. Varela, Online Data Processing for CERN industrial systems (12 November). Presented at Siemens Analytics Workshop, Munich, 2018.