by Ouijdane Guiza, Christoph Mayr-Dorn, Georg Weichhart, Michael Mayrhofer, Bahman Bahman Zangi, Alexander Egyed, Björn Fanta, Martin Gieler
Abstract:
Unforeseen situations on the shopfloor cause the assembly process to divert from its expected progress. To be able to overcome these deviations in a timely manner, assembly process monitoring and early deviation detection are necessary. However, legal regulations and union policies often limit the direct monitoring of human-intensive assembly processes. Grounded in an industry use case, this paper outlines a novel approach that, based on indirect privacy-respecting monitored data from the shopfloor, enables the near real-time detection of multiple types of process deviations. In doing so, this paper specifically addresses uncertainties stemming from indirect shopfloor observations and how to reason in their presence.
Reference:
Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes (Ouijdane Guiza, Christoph Mayr-Dorn, Georg Weichhart, Michael Mayrhofer, Bahman Bahman Zangi, Alexander Egyed, Björn Fanta, Martin Gieler), In 19th IEEE International Conference on Industrial Informatics, INDIN 2021, Palma de Mallorca, Spain, July 21-23, 2021, IEEE, 2021.
Bibtex Entry:
@Conference{Guiza2021a,
author = {Ouijdane Guiza and Christoph Mayr-Dorn and Georg Weichhart and Michael Mayrhofer and Bahman Bahman Zangi and Alexander Egyed and Björn Fanta and Martin Gieler},
booktitle = {19th {IEEE} International Conference on Industrial Informatics, {INDIN} 2021, Palma de Mallorca, Spain, July 21-23, 2021},
title = {Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes},
year = {2021},
pages = {1--8},
publisher = {{IEEE}},
abstract = {Unforeseen situations on the shopfloor cause the assembly process to divert from its expected progress. To be able to overcome these deviations in a timely manner, assembly process monitoring and early deviation detection are necessary. However, legal regulations and union policies often limit the direct monitoring of human-intensive assembly processes. Grounded in an industry use case, this paper outlines a novel approach that, based on indirect privacy-respecting monitored data from the shopfloor, enables the near real-time detection of multiple types of process deviations. In doing so, this paper specifically addresses uncertainties stemming from indirect shopfloor observations and how to reason in their presence.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/indin/GuizaMWMZEFG21.bib},
doi = {10.1109/INDIN45523.2021.9557502},
file = {:Conferences/INDIN 2021 - Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes/Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes - preprint.pdf:PDF},
keywords = {Pro2Future},
timestamp = {Mon, 18 Oct 2021 17:08:59 +0200},
url = {https://doi.org/10.1109/INDIN45523.2021.9557502},
}