Modern machines continuously log status reports over long periods of time, which are valuable data to optimize working routines. Data visualization is a commonly used tool to gain insights into these data, mostly in retrospective, e.g. to determine causal dependencies between faults of different machines. We present an approach to bring such visual analyses to the shop floor to support reacting to faults in real time. This approach combines combines spatio-temporal analyses of time series using a handheld touch device with augmented reality for live monitoring. Important information augments machines directly in their real-world context and detailed logs of current and historical events are displayed on the handheld device. In collaboration with an industry partner, we designed and tested our approach on a live production line to obtain feedback from operators. We compare our approach for monitoring and analysis with existing solutions that are currently deployed.
%0 Journal Article
%1 9732172
%A Becher, Michael
%A Herr, Dominik
%A Müller, Christoph
%A Kurzhals, Kuno
%A Reina, Guido
%A Wagner, Lena
%A Ertl, Thomas
%A Weiskopf, Daniel
%D 2022
%J IEEE Computer Graphics and Applications
%K myown visus:becherml visus:ertl visus:kurzhako visus:mueller visus:reina visus:weiskopf
%P 1-1
%R 10.1109/MCG.2022.3157961
%T Situated Visual Analysis and Live Monitoring for Manufacturing
%X Modern machines continuously log status reports over long periods of time, which are valuable data to optimize working routines. Data visualization is a commonly used tool to gain insights into these data, mostly in retrospective, e.g. to determine causal dependencies between faults of different machines. We present an approach to bring such visual analyses to the shop floor to support reacting to faults in real time. This approach combines combines spatio-temporal analyses of time series using a handheld touch device with augmented reality for live monitoring. Important information augments machines directly in their real-world context and detailed logs of current and historical events are displayed on the handheld device. In collaboration with an industry partner, we designed and tested our approach on a live production line to obtain feedback from operators. We compare our approach for monitoring and analysis with existing solutions that are currently deployed.
@article{9732172,
abstract = {Modern machines continuously log status reports over long periods of time, which are valuable data to optimize working routines. Data visualization is a commonly used tool to gain insights into these data, mostly in retrospective, e.g. to determine causal dependencies between faults of different machines. We present an approach to bring such visual analyses to the shop floor to support reacting to faults in real time. This approach combines combines spatio-temporal analyses of time series using a handheld touch device with augmented reality for live monitoring. Important information augments machines directly in their real-world context and detailed logs of current and historical events are displayed on the handheld device. In collaboration with an industry partner, we designed and tested our approach on a live production line to obtain feedback from operators. We compare our approach for monitoring and analysis with existing solutions that are currently deployed.},
added-at = {2022-03-28T16:28:12.000+0200},
author = {Becher, Michael and Herr, Dominik and Müller, Christoph and Kurzhals, Kuno and Reina, Guido and Wagner, Lena and Ertl, Thomas and Weiskopf, Daniel},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/25c369c7eec77eb8f3d65d29ca9e63bb6/guidoreina},
doi = {10.1109/MCG.2022.3157961},
interhash = {d58cae990c747de628816983dc624f92},
intrahash = {5c369c7eec77eb8f3d65d29ca9e63bb6},
issn = {1558-1756},
journal = {IEEE Computer Graphics and Applications},
keywords = {myown visus:becherml visus:ertl visus:kurzhako visus:mueller visus:reina visus:weiskopf},
pages = {1-1},
timestamp = {2022-03-28T14:28:12.000+0200},
title = {Situated Visual Analysis and Live Monitoring for Manufacturing},
year = 2022
}
