@inproceedings{SchneiderRufLermeretal.2023,
  author    = {Niels Schneider and Philipp Ruf and Matthias Lermer and Christoph Reich},
  title     = {ARTHUR: Machine Learning Data Acquisition System with Distributed Data Sensors},
  series = {Proceedings of the 13th International Conference on Cloud Computing and Services Science CLOSER, April 26-28, 2023, Prague, Czech Republic - Volume 1},
  isbn      = {978-989-758-650-7},
  doi       = {10.5220/0011747100003488},
  pages     = {155 -- 163},
  year      = {2023},
  abstract  = {On the way to the smart factory, the manufacturing companies investigate the potential of Machine Learning approaches like visual quality inspection, process optimisation, maintenance prediction and more. In order to be able to assess the influence of Machine Learning based systems on business-relevant key figures, many companies go down the path of test before invest. This paper describes a novel and inexpensive distributed Data Acquisition System, ARTHUR (dAta collectoR sysTem witH distribUted sensoRs), to enable the collection of data for AI-based projects for research, education and the industry. ARTHUR is arbitrarily expandable and has so far been used in the field of data acquisition on machine tools. Typical measured values are Acoustic Emission values, force plate X-Y-Z force values, simple SPS signals, OPC-UA machine parameters, etc. which were recorded by a wide variety of sensors. The ARTHUR system consists of a master node, multiple measurement worker nodes, a local streaming system and a gateway that stores the data to the cloud. The authors describe the hardware and software of this system and discuss its advantages and disadvantages.},
  language  = {en}
}