Deviation Detection in Production Processes based on Video Data using Unsupervised Machine Learning Approaches

Mühlbauer M, Epp H, Würschinger H, Hanenkamp N (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Procedia CIRP Curran

Publisher: Elsevier B.V.

Book Volume: 112

Pages Range: 162-167

Conference Proceedings Title: Procedia CIRP

Event location: Naples IT

DOI: 10.1016/j.procir.2022.09.066

Abstract

The detection of deviations within production processes is essential to ensure high productivity or avoid potential damage. Various approaches are available for this purpose. In the field of video surveillance, unsupervised machine learning methods have made significant progress in detecting deviations. In this paper, the transferability of these generic approaches to production processes is investigated. At first, an evaluation basis is created. Therefore, the variety of deviations, which can occur in an automated production process, is structured and covered as far as possible in video benchmark data sets. Subsequently, existing unsupervised approaches are selected, adapted and tested on the created data sets. In conclusion, the results show that the two chosen unsupervised autoencoder architectures can be partially used for generic deviation detection in the production domain. The main challenges identified are the large variety of different tasks and deviations in production processes. However, for further investigations, the development of even more detailed benchmark sets is essential.

Authors with CRIS profile

Matthias Mühlbauer Lehrstuhl für Ressourcen- und Energieeffiziente Produktionsmaschinen Hubert Würschinger Lehrstuhl für Ressourcen- und Energieeffiziente Produktionsmaschinen Nico Hanenkamp Lehrstuhl für Ressourcen- und Energieeffiziente Produktionsmaschinen

How to cite

APA:

Mühlbauer, M., Epp, H., Würschinger, H., & Hanenkamp, N. (2022). Deviation Detection in Production Processes based on Video Data using Unsupervised Machine Learning Approaches. In Roberto Teti, Doriana D'Addona (Eds.), Procedia CIRP (pp. 162-167). Naples, IT: Elsevier B.V..

MLA:

Mühlbauer, Matthias, et al. "Deviation Detection in Production Processes based on Video Data using Unsupervised Machine Learning Approaches." Proceedings of the 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering, ICME 2021, Naples Ed. Roberto Teti, Doriana D'Addona, Elsevier B.V., 2022. 162-167.

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