Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes

Quijdane Guiza; Christoph Mayr-Dorn; Georg Weichhart; Michael Mayrhofer; Bahman Bahman-Zangi; Alexander Egyed; Björn Fanta; Martin Gieler

doi:10.1109/INDIN45523.2021.9557502

Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes

Quijdane Guiza, Christoph Mayr-Dorn, Georg Weichhart, Michael Mayrhofer, Bahman Bahman-Zangi, Alexander Egyed, Björn Fanta, Martin Gieler

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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.

Original language	English
Title of host publication	19th IEEE International Conference on Industrial Informatics, INDIN 2021, Palma de Mallorca, Spain, July 21-23, 2021
Publisher	IEEE
Pages	1-8
Number of pages	9
DOIs	https://doi.org/10.1109/INDIN45523.2021.9557502
Publication status	Published - Jul 2021

Fields of science

102 Computer Sciences
102022 Software development

JKU Focus areas

Digital Transformation

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10.1109/INDIN45523.2021.9557502

Pro2Future - Products and Production Systems of the Future
Egyed, A. (Researcher), Küng, J. (Researcher), Miethlinger, J. (Researcher), Müller, A. (Researcher), Schlacher, K. (Researcher), Streit, M. (Researcher) & Ferscha, A. (PI)
01.04.2017 → 31.03.2025
Project: Funded research › FFG - Austrian Research Promotion Agency

Cite this

Guiza, Q., Mayr-Dorn, C., Weichhart, G., Mayrhofer, M., Bahman-Zangi, B., Egyed, A., Fanta, B., & Gieler, M. (2021). Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes. In 19th IEEE International Conference on Industrial Informatics, INDIN 2021, Palma de Mallorca, Spain, July 21-23, 2021 (pp. 1-8). IEEE. https://doi.org/10.1109/INDIN45523.2021.9557502

@inproceedings{9ed3eb88f8b240dc89f89c5576a57c8d,

title = "Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes",

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.",

author = "Quijdane Guiza and Christoph Mayr-Dorn and Georg Weichhart and Michael Mayrhofer and Bahman Bahman-Zangi and Alexander Egyed and Bj{\"o}rn Fanta and Martin Gieler",

year = "2021",

month = jul,

doi = "10.1109/INDIN45523.2021.9557502",

language = "English",

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booktitle = "19th IEEE International Conference on Industrial Informatics, INDIN 2021, Palma de Mallorca, Spain, July 21-23, 2021",

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Guiza, Q, Mayr-Dorn, C, Weichhart, G, Mayrhofer, M, Bahman-Zangi, B, Egyed, A, Fanta, B & Gieler, M 2021, Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes. in 19th IEEE International Conference on Industrial Informatics, INDIN 2021, Palma de Mallorca, Spain, July 21-23, 2021. IEEE, pp. 1-8. https://doi.org/10.1109/INDIN45523.2021.9557502

Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes. / Guiza, Quijdane; Mayr-Dorn, Christoph; Weichhart, Georg et al.
19th IEEE International Conference on Industrial Informatics, INDIN 2021, Palma de Mallorca, Spain, July 21-23, 2021. IEEE, 2021. p. 1-8.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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T1 - Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes

AU - Guiza, Quijdane

AU - Mayr-Dorn, Christoph

AU - Weichhart, Georg

AU - Mayrhofer, Michael

AU - Bahman-Zangi, Bahman

AU - Egyed, Alexander

AU - Fanta, Björn

AU - Gieler, Martin

PY - 2021/7

Y1 - 2021/7

N2 - 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.

AB - 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.

U2 - 10.1109/INDIN45523.2021.9557502

DO - 10.1109/INDIN45523.2021.9557502

M3 - Conference proceedings

SP - 1

EP - 8

BT - 19th IEEE International Conference on Industrial Informatics, INDIN 2021, Palma de Mallorca, Spain, July 21-23, 2021

PB - IEEE

ER -

Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes

Abstract

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Projects

Pro2Future - Products and Production Systems of the Future

Cite this