Abstract
The progress of digitalization and Internet of Things enables more and more complex, networked and powerful Cyber-physical Systems (CPS) operating in uncertain environments. This complexity and uncertainty, however, makes it unfeasible to model every aspect in advance. This causes the models to leave their scope and reach their capability limits. Specifically, in safe maintenance planning for highly-automated trucks, this fact causes waste of valuable resource, since maintenance models are often more rule-of-thumb (e.g. operation hours) than precise. In order to counteract this issue, we propose extending the digital twin concept by artificial intelligence such that the models become dynamic and adaptive. Having described the general approach and its architecture, we showcase and evaluate the approach in a highly-automated truck scenario.
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