It takes a machine to know a machine: AI-based condition monitoring for Power-to-X plants

In this webinar, we share results from the DynFlex project, where we developed AI-based methods for condition monitoring and early fault detection in PtX facilities. Without heavy maths, we’ll explain how a deep-learning model can learn what “normal operation” looks like and flag deviations as they emerge.

We’ll show how we tested the approach on simulated chemical-reactor processes, including a methanol reactor digital twin: how the model maps a plant’s operating modes, how different faults show up, and how quickly they can be detected.

Finally, we’ll look ahead: beyond raising alarms, the same model could give operators an intuitive “map” of the plant’s state and help explain why something looks wrong, a step towards more transparent predictive maintenance.