Predictive maintenance is a pretty hot topic. Not surprising: Using machine and process data, companies can identify problems in the production process at an early stage, maintain their plant in line with requirements, and hence avoid costly downtime. Numerous companies are already taking advantage of this potential and achieving significant improvements: In a survey of more than 200 companies in the DACH region, they estimated that they were able to reduce downtime by 18, and their maintenance and service costs by 17 percent. Learn in the white paper how Aurubis AG uses IoT to take advantage of predictive maintenance, proactively maintain its plants, avoid production downtime, and reduce expensive repair work.
The anode casting wheel plays a crucial role in copper production: Here, the molten copper is cast into anode molds. A lifting device then takes the cast anodes out of the molds of the casting wheel – up to 1,500 times a day. The repetitive motion sequences place enormous strain on the gripper arms of the device. If signs of wear are not detected in time, this could damage the system or the anodes themselves. This is where IoT comes into play: With the help of motion data, anomalies in the motion sequence of the gripper arms are to be detected at an early stage to carry out the necessary maintenance work. This positively affects the availability, performance, and overall equipment effectiveness (OEE) of the lifting device.