PROFINET and OPC UA enable practical predictive maintenance

Mining a treasure trove of data at AUDI

Robots making cars at AUDIPress information – new feature article


Unplanned downtimes always slow down production. There are a plethora of ideas and approaches for implementing preventive maintenance at production companies, to be sure, but they often fail in practice. The challenge mainly lies in accessing the data which could point out a flaw and then processing it in such a way that it can also be used. An experience-based example from Audi demonstrates that precisely these opportunities are opened up by combining PROFINET and OPC UA. 

Everyone’s talking about predictive maintenance. When it comes to implementing it in practice, though, many users often fail because they don’t have access to the right data. Mathias Mayer at Audi Neckarsulm was faced with this very situation. His experience showed that “90 percent of the data in body construction isn’t used or accessed.” This usually resulted in an additional sensor being required. That’s not the path Mayer wanted to go down. Quite to the contrary, he thought, “Let’s process the unused data first. If an additional sensor really ends up being necessary, I’d certainly be willing to talk about it.”

To Mayer, better utilization of available data is the most important requirement for reducing downtimes and working more efficiently. This is going to become even more decisive, as the complexity of production processes and the degree of automation will continue increasing in the near future. Why is data collection so difficult, though? A glimpse into body construction at the Neckarsulm site reveals the challenge. It’s at this location where A4, A6, A7, A8, R8 and A5 Cabrio model Audis are assembled by around 2,500 industrial robots. Each individual system is controlled via a PLC. “We always see the PLC as a puppet master making up to ten robots dance,” said Mayer in describing the situation in his division. The actual value creation takes place at the robot, which is why access to robot data is so immensely important.

In addition to the large number of plants involved, the various different production methods used also make data access and evaluation more difficult. For example, reducing weight while retaining maximum durability can only be achieved by combining different materials. This entails the use of a variety of different connection technologies. A plethora of joining technologies are used just for the new A8 alone, ranging from a very wide variety of welding processes to glueing to riveting – all told, 15 different processes need to be coordinated. Should production falter, experts in each of these individual processes are needed. This ends up being very expensive and time-consuming when you consider three-shift production, as a large number of employees would need to be trained and qualified.

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