Authors:
Olaf Graeser
;
Barath Kumar
;
Oliver Niggemann
;
Natalia Moriz
and
Alexander Maier
Affiliation:
Hochschule Ostwestfalen-Lippe, Germany
Keyword(s):
Automation technology, Modelling, Simulation, HIL test and AutomationML.
Related
Ontology
Subjects/Areas/Topics:
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Modeling, Simulation and Architectures
;
Robotics and Automation
;
Systems Modeling and Simulation
Abstract:
The growing complexity of production plants leads to a growing complexity of the corresponding automation systems. Developers of such complex automation systems are faced with two significant challenges: (i) The control devices have to be tested before they are used in the plant. For this, offline- and hardware–in–the loop (HIL) simulations can be used. (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 system model, i.e. a joint 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—allowing them to detect discrepancies between normal and observed behavior. System models cannot be modelled by one person in a single development step. Instead, such models must mirror the modularity of modern plants and
automation systems. Here, the new standard AutomationML is used as basis for such a modular system model. But a modular system model is only a first step: Both testing and diagnosis require the simulation of such models. Therefore, a corresponding modular simulation system for AutomationML models is presented here; for this, the Functional Mock–Up Unit (FMU) standard is used. A prototypical tool chain and a model factory (MF) is used to show results for this modular testing and diagnosis approach.
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