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Modern large-scale automation systems integrate thousands to hundreds ofthousands of physical sensors and actuators. Demands for more flexiblereconfiguration of production systems and optimization across differentinformation models, standards and legacy systems challenge current systeminteroperability concepts. Automatic semantic translation across informationmodels and standards is an increasingly important problem that needs to beaddressed to fulfill these demands in a cost-efficient manner under constraintsof human capacity and resources in relation to timing requirements and systemcomplexity. Here we define a translator-based operational interoperabilitymodel for interacting cyber-physical systems in mathematical terms, whichincludes system identification and ontology-based translation as special cases.We present alternative mathematical definitions of the translator learning taskand mappings to similar machine learning tasks and solutions based on recentdevelopments in machine learning. Possibilities to learn translators betweenartefacts without a common physical context, for example in simulations ofdigital twins and across layers of the automation pyramid are brieflydiscussed.

Interoperability and machine-to-machine translation model with mappings to machine learning tasks