Predictive maintenance for printers, a new activity being supported by EIT Digital as part of its OEDIPUS High Impact Initiative, will bring a shift in the performance and durability of commercial printing equipment.
In the current commercial printing environment, commercial printers need to be able to deliver constant uptime at the customer location. To avoid service interruptions, manufacturers have mainly emphasized corrective and preventive maintenance. Corrective maintenanance is about fixing a machine when it really is broken, while preventive maintenance involves replace parts when they are about to exceed their expected lifespan for error-free service.
However, these two approaches do not satisfy the growing need of commercial printing services for reliability and predictability in their production systems.
"Reducing downtime has always been very important. But in today's fast-paced economy, in which businesses calibrate production based on actual market demand and then need to satisfy it almost in real time, it has become crucial," Petri Liuha, EIT Digital's Digital Industry Action Line leader, says. "Just think of a digital bookstore whose success is highly dependent on how quick it is able to deliver - and print - the items for customers."
"Using sensor data produced by commercial printer, and analyzing this with an algorithm, makes it possible to determine when a part, or multiple parts, are starting to fail, so corrective actions can be taken," Rob Kersemakers, software team lead of Océ-Technologies B.V., the initiative's business champion, explains.
The algorithm itself is currently being developed by DFKI based on a large set of different data coming from different printers, and spanning several months of operations.
To store, maintain and analyse this information, the predictive maintenance for printers solution will benefit from the connection to an industry cloud platform for data analytics, developed by EIT Digital partners.
"We're now checking which features in this large data set might be interesting for a predictive algorithm. This will lead to a point where we have collected so much information that we could, based on this data set, do a prediction," says DFKI's Jens Haupert.
A first working prototype of the whole system is scheduled to be showcased by the end of this year.