Machine learning for detection of anomalies in press-hardening: Selection of efficient methods
2018 (English)In: Procedia CIRP, 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.2018.03.221ISI: 000526120800182Scopus 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)
2018-06-292018-06-292024-09-04Bibliographically approved