Cited by 201Machine Fault Diagnosis TechniquesAnomaly Detection Techniques and ApplicationsFault Detection and Control Systems

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

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Yan, Shen, Shao, Haidong, Xiao, Yiming, Liu, Bin, Wan, Jiafu·2023·Robotics and Computer-Integrated Manufacturing

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Research works printed but not linked: 4

  1. Wang G, Zhang F. A Sequence-to-Sequence Model With Attention and Monotonicity Loss for Tool Wear Monitoring and Prediction, IEEE Transactions on Instrumentation and Measurement, 70 (2021) 1-11.
  2. Zong B, Song Q, Min M R, et al. Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection, International Conference on Learning Representations, (2018).
  3. Gong D, Liu L, Le V, et al. Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection, Proceedings of the IEEE/CVF International Conference on Computer Vision, (2019) 1705-1714.
  4. Li X, Lim B S, Zhou J H, et al. Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation, Annual Conference of the PHM Society, 1 (1) (2009).

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Authors
Yan, S., Shao, H., Xiao, Y., Liu, B., & Wan, J.
Affiliations
  • Hunan University (CN)
  • University of Strathclyde (GB)
  • South China University of Technology (CN)
Corresponding author
Shao, Haidong
Venue
Robotics and Computer-Integrated Manufacturing
Publisher
Elsevier BV
Published
2023-02
Volume
79
Pages
102441
ISSN
0736-5845 (ISSN-L)
OpenAlex
W4292290851
Cited by
201
References
52
Citations by year
2026: 20 · 2025: 41 · 2024: 67 · 2023: 65 · 2022: 8
FWCI
18.23
Type
journal article
Language
en
Locations
  • Robotics and Computer-Integrated Manufacturing (publishedVersion)
  • Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
Keywords
unsupervised anomaly detection, machine tools, convolutional autoencoder, noise robustness, sensor fusion, fault detection

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