Cebi has entered into a four-year collaborative research partnership with SnT researchers
Photo: Jessica Theis/Archives
Processes at automotive manufacturers Cebi are about to get more efficient thanks to a new project with the University of Luxembourg’s Interdisciplinary Centre for Security, Reliability and Trust (SnT).
Steinsel-based Cebi has entered into a four-year collaborative research partnership, which aims to bring the firm’s flagship factory in line with industry 4.0 principles, and increase equipment effectiveness. Industry 4.0 enables automated processes to share their data, communicate with and even control other machines.
SnT researchers will work with Cebi Luxembourg to enable machines in the factory to communicate within a shared virtual space. Machine learning algorithms can use this data to monitor the factory’s overall performance in real time, understand and predict technical issues, and advise engineers on what to do about them. The barriers to adopting such an approach, however, are still very high.
Making different data compatible
“For decades individual suppliers have built factory machinery using their own proprietary software, systems and communication protocols,” says SnT researcher and industrial networking expert Jérémy Robert.
“As factories use machines from multiple brands, it’s as if they’re all speaking different languages. The data they produce is incompatible, so the machines at stages one and two of the production process, for example, can’t talk to one another. We need to analyse the existing hardware and software systems, and develop an ‘open source’ IT infrastructure to break down these ‘language’ barriers.”
Once this raw data is all in one place, the second stage of the project involves turning it into actionable information. Dr Robert and his team will work with SnT spin-off company DataThings, which specialises in live machine learning and real-time analysis of sensor data, to process the huge amount of information the factory produces on a daily basis.
“These ‘machine learning’ algorithms will learn from patterns over time in order to know whether the factory is performing efficiently,” Robert said.
Cebi executive board member Paul Elvinger said: “These tools range from live monitoring of different generations of machine tools and performance indicators, to more advanced digital services such as predictive maintenance, manufacturing system optimisation and autonomous factory operations.”
Although it will be several years before this research can enable large-scale technology deployment across different shop floors of the Cebi Group, its findings will remove major barriers for digital manufacturing.