Abstract
AbstractIn this paper, a new information model for machine learning applications is introduced, which allows for a consistent acquisition and semantic annotation of process data, structural information and domain knowledge from industrial productions systems. The proposed information model is based on Industry 4.0 components and IEC 61360 component descriptions. To model sensor data, components of the OGC SensorThings model such as data streams and observations have been incorporated in this approach. Machine learning models can be integrated into the information model in terms of existing model serving frameworks like PMML or Tensorflowgraph. Based on the proposed information model, a tool chain for automatic knowledge extraction is introduced and the automatic classification of unstructured text is investigated as a particular application case for the proposed tool chain.
Highlights
Data in industrial production systems is usually stored in a heterogeneous way, using a large variety of data formats and semantics
A new information model for machine learning applications is introduced, which allows for a consistent acquisition and semantic annotation of process data, structural information and domain knowledge from industrial productions systems
Components of the OGC SensorThings model such as data streams and observations have been incorporated in this approach
Summary
Data in industrial production systems is usually stored in a heterogeneous way, using a large variety of data formats and semantics The integration of these data sources, which cover besides process data structural information, domain knowledge and process documents, is an essential prerequisite for the successful application of machine learning and optimization methods in the context of industrial production. Examples are the reference architecture Industry 4.0 (RAMI4.0) [3], modeling languages, which allow for a structured and component-based of production plants, such as AutomationML [9], and industrial communications standards with information modeling capabilities such as OPC-UA [6] Such general approaches are usually not tailored to the specific requirements of machine learning approaches.
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