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Improving efficiency in the plant construction process and providing valid resulting plant models requires a technology that validates planning and data exchange formats for plant engineering, such as CAEX (Computer Aided Engineering Exchange). Semantic Web technologies support validation mechanisms for querying and reasoning over domain models expressed in form of ontologies. In this paper, we present an approach to the automated validation of CAEX plant models by their transformation to ontologies and subsequent application of Semantic Web reasoning for validation purposes.

The manufacturing domain currently experiences a significant increase in resource expenses for industrial plants. However, the implementation of systems to monitor the resource consumption in such complex plants requires high investment concerning time and manual effort. Our goal is to describe the plant by means of knowledge-based models and rules to implement a generic, semiautomated monitoring system which can be defined with lower initial effort and which can be adapted quickly to modifications. An advantage of this model-based approach is that the energy and resource consumption of each component in a plant can be associated with a sequence of operations and the effects on the overall system get visible. Another advantage of knowledge-based systems combined with rules is that they offer application independent solutions and flexibility. The paper outlines the state of the art of relevant technologies by describing several approaches, such as existing monitoring systems, rule engines and modeling tools. Furthermore, it describes a representative example that we will use in our further work to evaluate which tools are appropriate for a resource monitoring system.

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The planning and engineering of industrial plants is characterized by a heterogeneous landscape of tools and data formats covering multiple engineering aspects, such as electrical engineering, mechanical engineering, software engineering, etc. To provide plant engineers in different roles with an integrated view on engineering data, we propose a conceptual modelling approach based on MOF layering for representing plant knowledge across multiple disciplins. Furthermore, we suggest to instantiate this conceptual modelling approach on a semantic technology stack featuring semantic data representation based on RDF and web-based plant navigation in a semantic Wiki.

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