Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises
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Cited by
Sorted by citations
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“This method has seen significant advancements with the introduction of autoen-coders [171], [172] and Generative Adversarial Networks (GANs) [173], [174], [175], which have notably expanded unsupervised learning’s applicability, enabling more sophisticated data generation and representation learning…”
Method
- 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
- Open access
- Licence
- 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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