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

• Published in: Robotics and computer-integrated manufacturing Vol. 79; p. 102441
• Main Authors: Yan, Shen, Shao, Haidong, Xiao, Yiming, Liu, Bin, Wan, Jiafu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2023
• Subjects: 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

•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...