An innovative system that will help industry companies predict the need for maintenance and thus save money and work. That is the goal of a new project, led by Professor Basim Al-Najjar at Linnaeus University, starting November 1st.
What if you could know when a component in a machine is about to break. Then it would be possible to replace it at the right time – not unnecessarily early, nor once it has stopped working, facing consequences like subsequent errors, interruptions and costs. A wishful dream for industry, but also a scenario not too far away.
“Cheaper and more powerful sensors and new methods for analysing large data volumes (Big Data) provide completely new opportunities to investigate the performance and state of machines in industry,” says Basim Al-Najjar, Professor of Terotechnology at Linnaeus University.
Developing and testing a predictive, intelligent decision support system for different areas of industry is the goal of the new project Predictive Cognitive Maintenance Decision Support System (PreCoM), for which Basim Al-Najjar is the coordinator. The system should be able to identify damage to machines and estimate their scope and consequences, calculate the useful life of components, increase accuracy when discovering damages, and report when preventive maintenance is needed. One goal of the project is to reduce the maintenance time of the machines by at least 10 per cent.
“It’s not just about saving money and improving the working environment of the manufacturing companies. Machine manufacturers can also benefit from collected data, to improve the quality of the machines they make,” says Basim Al-Najjar.
The field is also noted at EU level, in the form of the announcement Novel design and predictive maintenance technologies for the increased operating life of production systems under the EU Horizon 2020 research and innovation program. Among 57 applications for this call, PreCoM was one of three projects that were allocated funding a few months ago – just over € 6.1 million.
“At Linnaeus University we have been pioneers in smart cognitive maintenance and its usefulness. The decision support system I’ve developed, the Smart eMaintenance Decision Support System, and the continued research on this in PreCoM will be a hub in the project,” says Basim Al-Najjar.
The consortium behind the project consists of 17 partners from Sweden, Germany, Spain, Greece, France and Slovakia: manufacturing companies for testing the system, manufacturers of machine tools and of components, innovative small businesses, research institutes, two German universities and Linnaeus University, coordinator in the project.
For more information, contact
Basim Al-Najjar, Professor of Terotechnology, +46 (0)70-765 30 51, email@example.com
Anders Runesson, Research Communications Officer, +46 (0)70 81 70, firstname.lastname@example.org