The Steinbeis Foundation's Transfer Award | The Löhn Award 2019

Kyana – Predictive Maintenance Using Digital Twins

As artificial intelligence (AI), enhanced digital imaging, and new ways of interacting between systems continue to merge, this is paving the way for innovative product extensions – ideas that could be extremely useful in areas such as training, monitoring, and maintenance. Systems that can run checks on themselves reduce the need for service technicians to work on site, which not only improves machine availability but also makes it possible to operate systems much more economically.

As part of a partnership with Koenig & Bauer Coding, the Steinbeis Research Center for Design and Systems in Würzburg has developed Kyana, a digital extension to a labeling system called alphaJET. The alphaJET solution is a high-speed, ultimate-accuracy continuous inkjet printer that sprays codes onto products, simultaneously drawing on variable data directly on the production line. Kyana is an AI-based software solution that communicates through voice commands and uses augmented reality (AR) to depict the complex internal mechanisms of printing systems using clear, interactive images. In the future, Kyana will work like a smart assistant capable of taking on a variety of tasks from training, to controlling devices, explaining maintenance processes or servicing procedures, and spotting material wear and consumption levels early. In parallel to this, over time the system learns how to analyze all kinds of external influences and draw on this information to ensure it maintains high printing quality and maximum availability.

By using AR, Kyana assumes its own persona. This expanded visual presence makes it easier to understand hardware and how it works. By using digital overlays, the system allows users to look at precise details inside the printing system. Combined with speech output, this simplifies maintenance work and repairs. The extended AI functionality also makes it possible to equip systems with a virtual hand, which can save a lot of legwork by using a digital twin when a device requires remote maintenance. Ideally, this should make it possible to address faults more quickly and avoid long and expensive journeys for service personnel.

This solution offers huge potential, also by analyzing acquired data and thus providing valuable resources for future applications, and this has earned the innovation the Steinbeis Foundation Transfer Award – the Löhn Award. The strong mutual trust between the two parties involved in the partnership is laying the ideal foundation for this potential.

Getting in touch

Prof. Erich Schöls, Prof. Ulrich Braun, Dipl.-Des. Sebastian Gläser
Steinbeis Research Center Design and Systems

Kyana – Predictive Maintenance Using Digital Twins

Duration: 4 min.

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