Robust Digitalization of Manufacturing Applications

Important results from the project

The project has developed a method to identify critical functions that need to be monitored at component, machine and line level, and to assess the quality of collected data. The methods serve as a general tool for evaluating and improving data quality. The data can be used, among other things, to estimate the remaining life of equipment and to detect bottlenecks in production systems early, which contributes to increased productivity and higher operational reliability.

Expected long term effects

In the long term, the project´s outcomes are expected to enable early identification of deviations related to wear or other system effects, allowing for timely maintenance and reduced equipment downtime. More efficient and economic data management will optimize storage and processing, contributing to lower energy consumption. Together, these measures support improved system reliability, resource efficiency, and sustainability in industrial operations.

Approach and implementation

The project carried out both case studies and synthetic use cases, as well as industrial case studies with a focus on component, machine and system level. The work followed a triple helix model with close collaboration between academia, students and industry, which worked as planned. However, a challenge during the project was the access to data, as some development efforts were only implemented in the middle of the project, this affected some of the activity and the schedule