Predictive Maintenance

02-21-2024 1 min read

AI Summary

Predictive maintenance uses sensors, AI, and real-time data to detect potential machine failures before they occur. When unusual conditions or predefined thresholds are detected, companies can react early and avoid costly production downtime. Deutsche Telekom supports this with connectivity, cloud capacity, and smart monitoring solutions that analyze anomalies and help assess failure risks across entire machine fleets.

Knowing in advance when something will go wrong with your own machine: what probably almost everybody wants is possible today, thanks to Deutsche Telekom.

We are all surrounded at home by any number of machines. But only very few of us maintain their coffee machine, washing machine, or dishwasher regularly. Damage or wear and tear are then often only noticed when it’s actually too late.

The bigger and more expensive the machines, the more disastrous are the results. In industry, every machine failure or production downtime causes companies great expense. The magic words here are predictive maintenance.

This involves an early warning system. Many sensors within the machines produce huge volumes of data. Through the use of artificial intelligence, a particular threshold is specified that the machine is not to exceed.

As soon as this particular threshold is reached, the system sounds the alarm. Using this information, companies can then react quickly to problems or incidents. Technicians can intervene remotely or on the spot.

There are also Telekom solutions for problems that are already at an advanced stage. "Smart monitoring" allows in-house experts to examine possible anomalies, compare them with incidents that have already happened, and assess the entire stock of machines for the risk of possible failure.

All this assumes that huge data volumes are transferred in real time. For this you not only need the best connection, but also the relevant cloud capacity.