Speaker
Description
The European Spallation Source ERIC (ESS) is a joint European organisation committed to building and operating the world's leading facility for research using neutrons. The facility design and construction includes a powerful linear proton accelerator, a helium-cooled tungsten target wheel and two dozen state-of-the-art neutron instruments.
ESS is made up of a large number of diverse systems and disciplines, covering for example water cooling, vacuum, power distribution, timing systems, information technology, networking, microwaves, cryogenics among others. These systems are integrated and controlled by the central integrated control system (ICS), which acts as the “brain” of the ESS machine.
The ESS machine will generate at least an order of magnitude larger volumes of control system related data than typically existing in large industries. With an estimated number 100 000 devices to control and 1.6 million process values in the control system we realize that the high-level requirement of 95% availability for the facility will be very challenging and that ICS will play a key role to reach this goal. For this reason, ESS initiated a control system machine learning (CSML) project in 2018. The project will explore how application of modern machine learning technologies to a large-scale industrial distributed control system can help increase facility availability and efficiency and lower costs for operation.
This talk will cover the activities and outcome of the two first years in the project.
Title
Two years into the ESS Control System Machine Learning Project
| authors | K. Rathsman |
|---|---|
| affiliation | European Spallation Source ERIC |
