AutomationML as a shared model for offline- and realtime-simulation of production plants and for anomaly detection

Abstract

The growing complexity of production plants leads to a growing complexity of the corresponding automation systems. Two significant challenges have to be faced: (i) the control devices have to be tested before they are used in the plant. (ii) The diagnosis functions within the automation systems become more and more difficult to implement; this entails the risk of undetected errors. Both challenges may be solved using a joint system model of the plant and the automation system: (i) Offline simulations and HIL tests use such models as an environment model and (ii) diagnosis functions use such models to define the normal system behaviour. Modular models like this can be represented with AutomationML. Additionally, testing and diagnosis require the simulation of these models. Therefore, a corresponding simulation system for AutomationML models is presented here. A prototypical tool chain and a model factory (MF) are used to show results for this approach. © 2013 Springer-Verlag.