Why should an energy supply system talk to me? Maintenance and servicing for current crane systems is growing not only in size, but also control complexity. But there is no need to panic because, as you learned during your studies, you don’t need to know everything; you just need to know where to find the […]
Anyone who can make reliable, useful maintenance predictions is one step ahead of the competition. In the age of digitalisation, more than Big Data is required for such predictions. To derive correct maintenance scheduling recommendations from the sensor data, we therefore compare them with long-term empirical values from the igus® test database.
In automotive production, the line never stops. On average, an engine comes off the assembly line every 14 seconds. To avoid unplanned downtime and system breakdowns, many German and international automotive factories are already using modern maintenance concepts with predictive maintenance.
Because technologies supported by the Industrial Internet of Things are enjoying greater acceptance in factory environments, even more components with integrated intelligence are being produced. This is leading to the emergence of a new generation of smart plastics capable of constantly monitoring themselves and providing performance data and early warning of critical wear. These smart plastics can be used to increase plant productivity, maximise service life, and reduce costs thanks to condition monitoring and predictive maintenance.
More than 25,000 igus energy chains travel long distances every year in STS cranes, storage and retrieval units and linear robots. To load and unload the cutting-edge Triple E-class container ships, larger STS cranes must be built or existing cranes retrofitted. For future operations, in addition to using rol e-chains, there is much to recommend self-monitoring systems fitted with sensors.