DIREC project

Edge -based AI systems for predictive maintenance

Summary

Downtime of equipment is costly and a source of safety, security and legal issues. Today, organisations adopt a conservative schedule of preventive maintenance independent of the condition of equipment. This results in unnecessary service costs and occasional interruptions of production due to unexpected failures.

Therefore, it is imperative in many domains to transition from regular maintenance to predictive maintenance. The report ‘An AI Nation: Harnessing the Opportunity of Artificial Intelligence in Denmark’ estimates that enabling predictive maintenance via AI has a 14-19 billion potential for the private sector in Denmark.

Together with industrial partners, this project aims to uncover how AI and data-driven methods can solve the problem, so that companies have the least possible wasted time when production is at a standstill.

Project period: 2020-2024
Budget: DKK 12,24 million

More info

The research aim of the project is methods and tools that enable industry to develop new solutions with accurate AI-based maintenance predictions on edge-based software platforms.

The resulting applications must be deployed to collect and process large amounts of data locally. This data feeds high accuracy predictive models deployed at the edge, adapted to changing local conditions, and maintained with minimum intervention from operators.

These models should cover abstractions allowing the understanding of relevant dependencies in the data. The key research problem is to devise architectures and solutions that scale to the entire fleets of equipment with accurate AI predictions.

This also requires that resources in terms of processing power, storage and communication, are optimised in order to obtain low-power and real-time performance, leading to a resource efficiency vs prediction accuracy trade-off. The project will establish a bridge to enable Danish companies to develop and use AI-based predictive maintenance within several domains.

Scientific value

The scientific value of the project is new methods and tools and associated knowledge of their performance and properties in field tests. These are important contributions to provide excellent knowledge to Danish companies and education programs within AI and IoT.

Capacity building

For capacity building the value of the project is to educate 3 PhD students (including 1 Industrial PhD) and 1 Post Doc in close collaboration with industry. The open source availability of general project outcomes and industry collaboration enable several exploitation paths. In addition, for the master-level the projects will offer an industry program to 15 students at 3 universities.

Business and societal value

The business and societal value is on a national level estimated to a 14-19 billion potential for the Danish private sector. In the project we target both the medical, robotic, industrial and energy sectors. These are Danish frontrunners in adopting the technology and creating inspiration for wider adoption by the Danish private sector. For the public sector enabling equipment with higher operation efficiency will positively impact the efficiency of the sector.