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
• ISSN: 0736-5845; 1879-2537; 1879-2537
• DOI: 10.1016/j.rcim.2022.102441

•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 vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods. [Display omitted]