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Machine learning for detection of anomalies in press-hardening: Selection of efficient methods
Gestamp HardTech AB.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, ProcessIT Innovations R&D Centre.ORCID iD: 0000-0001-5564-2295
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, ProcessIT Innovations R&D Centre.ORCID iD: 0000-0002-2356-7830
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1079-1083Article in journal (Refereed) Published
Abstract [en]

The paper addresses machine learning methods, utilizing data from industrial control systems, that are suitable for detecting anomalies in the press-hardening process of automotive components. The paper is based on a survey of methods for anomaly detection in various applications. Suitable methods for the press-hardening process are implemented and evaluated. The result shows that it is possible to implement machine learning for anomaly detection by non-machine learning experts utilizing readily available programming libraries/APIs. The three evaluated methods for anomaly detection in the press-hardening process all perform well, with the autoencoder neural network scoring highest in the evaluation.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 72, p. 1079-1083
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-69984DOI: 10.1016/j.procir.2018.03.221Scopus ID: 2-s2.0-85049586782OAI: oai:DiVA.org:ltu-69984DiVA, id: diva2:1228934
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, 16-18 May 2018
Note

Konferensartikel i tidskrift;2018-06-29 (andbra)

Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2018-08-10Bibliographically approved

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Kyösti, PetterLindström, John

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