IM+io Fachmagazin, Ausgabe 4/2019

Bessere Produkte dank Künstlicher Intelligenz: 
Wie maschinelles Lernen die Qualität voraussagen kann

Steffen Klein, Tizian Schneider, Andreas Schütze

LITERATURANGABEN

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[4]VDMA, “Selbstlernende Systeme zur Anlagenüberwachung,” Am Puls der Maschine Cond. Monit., 2015. 

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[7]T. Schneider, S. Klein, and A. Schütze, “Machine learning in industrial measurement technology for detection of known and unknown faults of equipment and sensors,” tm – Tech. Mess., Sep. 2019. 

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[11]B. B. Thompson, R. J. Marks, J. J. Choi, M. A. El-Sharkawi, Ming-Yuh Huang, and C. Bunje, “Implicit learning in autoencoder novelty assessment,” in Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290), pp. 2878–2883. 

[12]Q. Wang, Q. Gao, X. Gao, and F. Nie, “Principal component analysis,” IJCAI Int. Jt. Conf. Artif. Intell., vol. 2, pp. 2936–2942, 2017. 

[13]T. Schneider, L. Schirmer, S. Klein, and A. Blum, “Combination of Human and Machine Intelligence to Optimize Assembly,” in Societal Automation: Technological & Architectural Frameworks, 2019.