Data analytics for industrial controls and monitoring

Project goal

The main goal of the project is to render the industrial control systems used for the LHC more efficient and more intelligent. The focus 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)
Rafal Kulaga
Team members
Filip Široký, Marc Bengulescu, Fernando Varela Rodriguez, Rafal Kulaga, Piotr Golonka, Peter Sollander
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

One of the main achievements in 2020 was the experimental deployment of the Siemens Distributed Complex Event Processing (DCEP) technology to enable advanced data analytics and predictive maintenance for the oxygen-deficiency sensors in the LHC tunnel. This was initially done by deploying a suite of microservices on a pool of virtual machines in the cloud. In the latest phase, the cloud-edge gap was bridged by also adding a Siemens IoT 2050 box as a worker to the computing pool. 

Progress was also made on the optimisation of the ion-beam source for the LINAC3 accelerator at CERN. Multiple machine-learning and spectral techniques were employed and extensively tested, under the guidance of LINAC3 experts. We expect to continue this work in 2021.

A prototype platform was developed to provide collection service for generic AI algorithms that could easily be employed by people who are not data scientists, helping them to perform advanced analytics on controls data. This is an ongoing effort together with colleagues from various groups at CERN, including the Cryogenics group in the Technology department and the Cooling and Ventilation group in the Engineering department.

Next steps

Depending on the priorities agreed with the company, the focus of the collaboration can shift to new areas, such as device management. A CERN-SIEMENS joint workshop has been scheduled in order to define the use cases for the following year.


    T. Breeman, R. Gan, L. Hoogendijk, P. Knops, O. Lynch, J. Roberts, J. Soons, M. Theodosiou, J. Tian, N. Warsen, Forecasting the LINAC3 Ion Beam Current (11 February). Presented at the BE Seminars, 2022.


    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. Široký, Data Analytics and IoT for Industrial Control Systems (22 January). Presented at CERN openlab technical workshop, Geneva, 2020.
    M. Bengulescu, F. Široký, Distributed Complex Event Processing at the Large Hadron Collider (2 July). Presented at IoT Siemens Conference, 2020.
    F. Široký, M. Bengulescu, Predicting LINAC3 current with LSTM and wavelet spectral analysis (22 September). Presented at Siemens Corporate Technology Seminar, 2020.
    M. Bengulescu, F. Široký, Predicting LINAC3 current using RNNs and spectral decomposition (19 June). Presented at CERN ML Coffee, Geneva, 2020.
    F. Široký, Bayesian ML – a practical approach (6 May). Presented at CERN ML Coffee, Geneva, 2020.
    M. Bengulescu, F. Široký, Data Analytics for Industrial Controls (31 January). Presented at CERN ML Coffee, Geneva, 2020.