ICAI The labs: AI for Autonomous Systems in the NL

In September, ICAI: The Labs is focused on AI for Autonomous Systems in the Netherlands. The EAISI AIMM lab and another ICAI lab each present their work and discuss challenges and developments in this field.

The EAISI AI-enabled Manufacturing and Maintenance (AIMM) Lab is a collaboration between Eindhoven University of Technology (TU/e), KMWE, Lely, Marel, and Nexperia. The lab's goal is to improve decision-making in manufacturing and maintenance using artificial intelligence. AIMM Lab is based in Eindhoven.

12.00 (noon): Opening

12:05 Introduction of the EAISI AIMM lab.

12:10 Geert-Jan van Houtum (TU Eindhoven) presents "A Predictive Maintenance Concept for Geographically Dispersed Technical Systems"

12.25 Introduction of the other ICAI Lab

12:30 Second talk on AI for Autonomous Systems in the Netherlands

12.45 Discussion of what’s next in AI for Autonomous Systems

13:00 End

"A Predictive Maintenance Concept for Geographically Dispersed Technical Systems"

Thanks to IoT, it is nowadays possible to remotely monitor the health status of technical systems. This information can be used to come to a so-called predictive maintenance concept.

Under such a concept, the aim is to replace a degrading component by a ready-for-use component just before a failure would occur. This can be done for components for which you can follow the degradation behavior or for which you can predict upcoming failures by some form of data analysis.

For other components, you may only have information on the lifetime distribution and replacement decisions have to be taken based on the age of the component. A system has generally a mix of components: lifetimes are given for a first group of components, degradation processes for a second group of components, and data- based failure predictions for a third group of components.

In addition, many systems are geographically dispersed and require an expensive visit of a service engineer for the execution of maintenance. Then maintenance actions for the various components have to be clustered in order to avoid high costs for engineer visits. We show how an appropriate predictive maintenance concept can be constructed for geographically dispersed technical systems. We will include a case at a manufacturer of agricultural high-tech equipment.