Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises

•A new FDD loss function to suppress the noises is designed.•Construct the PCDF module to enhance the robustness of the network.•The unsupervised anomaly detection of machine tools under noises.•The proposed HRCAE performs effective in the CNC machine tool. Anomaly detection of machine tools plays a...

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Veröffentlicht in:	Robotics and computer-integrated manufacturing Jg. 79; S. 102441
Hauptverfasser:	Yan, Shen, Shao, Haidong, Xiao, Yiming, Liu, Bin, Wan, Jiafu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2023
Schlagworte:	Deep learning Drift och underhållsteknik Hybrid robust convolutional autoencoder Machine tools Noises Operation and Maintenance Unsupervised anomaly detection Deep learning Machine tools Unsupervised anomaly detection Noises Hybrid robust convolutional autoencoder
ISSN:	0736-5845, 1879-2537, 1879-2537
Online-Zugang:	Volltext
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