After testing the concept at 20 print sites, Koenig & Bauer has launched predictive maintenance to its newspaper and commercial web users.
A range of press components including plate changers, reelstands and hydraulic clamping and lubrication systems can be assessed using AI-based methods such as rule mining or machine learning for real-time data analysis.
Even otherwise hidden processes and interactions of components or internal networks on a pressline can be evaluated systematically.
Service head Thomas Potzkai says objectives are clearly defined: ““We use the information contained in already-existing press data for automated analyses. This makes it possible to identify and rectify potential problems before they occur.”
Workflows have been “elaborated and implemented” with more than 20 pilot users in the newspaper and commercial segments.
Service managers are given full details of the situation on a given press, so that arrangements can be made for remote maintenance or service calls. A technician then rectifies a fault within a scheduled assignment, with any necessary downtime planned within an already production-free period.
Among new customers is Oldenburg-based WE-Druck in northern Germany, which installed a new Commander CL three years ago. Senior manager Margit Schweizer says predictive maintenance gives peace of mind.
“We cannot afford sudden stoppages and unscheduled downtimes. Our customers expect us to deliver top quality within the agreed deadlines.”
The predictive maintenance concept is also being offered for K&B’s digital and packaging presses and those of other business units.