Abstract
Preparing training data sets for supervised machine learning is particularly difficult when the input information has a serial nature and the data sources are non-deterministic. This paper discusses a problem related to preparing a data set for the machine learning algorithms that are used for Automated Guided Vehicles (AGV). An OPC UA server that is dedicated for machine learning support was designed in order to comply with the communication standards and information models that are used in the new generation of manufacturing systems. The proposed approach not only utilises communication features of OPC UA technology but also its rich possibilities for information modelling. The OPC UA server that was created converts raw input data into a format that can be easily applied for the machine learning process. The presented solution is dedicated for the AGV that are used for internal logistics in flexible production systems. The authors discuss the different information models that are available for the OPC UA standard and explain the design choices that were made when preparing the server. The presented solution was verified during the development process for a new family of AGV that is being designed and produced by the AIUT Company.
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Acknowledgements
(1) This work was supported by the Polish National Centre of Research and Development from the project “Knowledge integrating shop floor management system supporting preventive and predictive maintenance services for automotive polymorphic production framework” (grant agreement no: POIR.01.02.00-00-0307/16-00). The project is realised as Operation 1.2: “B+R sector programmes” of the Intelligent Development operational programme from 2014–2020 and is co-financed by the European Regional Development Fund.
(2) This publication was supported as part of the Rector’s grant in the field of scientific research and development works. Silesian University of Technology, grant no. 02/020/RGJ19/0169.
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Cupek, R., Gólczyński, Ł., Ziebinski, A. (2019). An OPC UA Machine Learning Server for Automated Guided Vehicle. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_19
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