Abstract
Sustainable collection, processing and storage of sensor data in industrial context are essential requirements for gaining long-term knowledge on product and process quality. Today, collected data is often stored in arbitrary data formats without appropriate metadata describing the data content and is therefore lost for future reuse because crucial information on how to find, access and interoperate with the data is missing. Moreover, insufficiently described or missing data can lead to wrong decisions. In a scientific context, the FAIR data principles are an emerging design guideline to provide all collected data with rich metadata that allow for finding, accessing, interoperating with and reusing the data effectively later. This publication proposes an approach to implement the FAIR principles for industrial sensor data. It analyses major deficits and challenges of making industrial sensor data FAIR and outlines a system architecture for acquisition of FAIR industrial sensor data, called FAIR sensor services.
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