| Title | Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Guiza, Ouijdane, Mayr-Dorn, Christoph, Weichhart, Georg, Mayrhofer, Michael, Zangi, Bahman Bahman, Egyed, Alexander, Fanta, Björn, Gieler, Martin |
| Conference Name | 2021 IEEE 19th International Conference on Industrial Informatics (INDIN) |
| Keywords | Assembly Processes, Correlation, Deviation Detection, Human Behavior, Human-Intensive, Industries, Law, Measurement, Metrics, Monitoring, Privacy Policies, process monitoring, pubcrawl, Regulation, Scalability, Uncertainty |
| 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. |
| DOI | 10.1109/INDIN45523.2021.9557502 |
| Citation Key | guiza_automated_2021 |
Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes