Unsupervised Anomaly Detection of Industrial Robots using Sliding-Window Convolutional Variational Autoencoder

With growing dependence of industrial robots, a failure of an industrial robot may interrupt current operation or even overall manufacturing workflows in the entire production line, which can cause significant economic losses. Hence, it is very essential to maintain industrial robots to ensure high-...

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Veröffentlicht in:	IEEE access Jg. 8; S. 1
Hauptverfasser:	Chen, Tingting, Liu, Xueping, Xia, Bizhong, Wang, Wei, Lai, Yongzhi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Anomalies Anomaly detection Automation Convolutional neural network Economic impact Industrial robots Mathematical model Mathematical models Multivariate analysis Real time Real-time systems Robot sensing systems Robots Service robots Shutdowns Sliding Sliding window Time dependence Time series Time series analysis Variational autoencoder
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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