Faster and More Efficient
Smart Maintenance makes it possible: Where previously a time-consuming error analysis by the maintenance technician was necessary, today the smartphone is sufficient. Instead of conducting time-consuming root cause analysis, obtaining documents, plans and spare parts, and covering numerous distances between plants, bases and warehouses, many things now run automatically. In the event of a standstill or fault, the responsible maintenance technician is alerted via his smartphone by sensors on the machine or a digitally created fault ticket by the production employee. This incoming fault message already describes the possible causes of the fault in detail. By scanning a QR code on the system, the expert receives a lot of additional relevant information within a few seconds, for example on previous maintenance or repairs. With the help of other apps, he can search for spare parts in the warehouse and reserve them for installation. If the wear part is not in stock, it can be ordered at the touch of a finger - all with the smartphone next to the system.
"Today, I have more time for the essentials, for working on the machine," explains maintenance engineer Michael Müller, who has been responsible for the machinery in the light metal foundry for many years. Landshut site manager Dr. Stefan Kasperowski emphasizes, "Digitizing our maintenance not only makes the work of the maintenance staff easier, but also enables more efficient, trouble-free production."
Detecting Problems Before They Arise
As with any form of digitization, the collection of millions of data forms the basis of smart maintenance applications. By networking the data, the largest areas for action are visualized and localized with the help of automated evaluations and reports, so that a data-based solution can be developed and, consequently, the level of disruptions can be sustainably reduced. Targeted plant monitoring also lays the foundation for "predictive" maintenance. Thanks to the data, maintenance experts can look into the future and, for example, detect an impending machine failure at an early stage. Necessary work can then be planned in such a way that downtime is reduced to a minimum, for example by scheduling a necessary plant stop during the non-production time. Maintenance is thus not carried out at set intervals, but when the digital monitoring system detects the need.
The Landshut light metal foundry is also increasingly relying on predictive maintenance: algorithms consider various influencing parameters such as temperature, current consumption, vibration, noise development, etc. to determine the probability of failure and remaining service life of technical systems and equipment. As a result, tools, for example, are no longer replaced across the board after a certain runtime, but according to the calculated condition.
