ArticleScientific reports2025
Unsupervised anomaly detection for gearboxes based on the deep convolutional support generative adversarial network.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Novel Residual Dual Attention Multiscale Network for Vibration-Based Damage Recognition in Floating Wind Turbine Structural Health Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Trend-Conditioned Residual Learning for Early Fault Warning in Nonstationary Multi-Sensor Oil Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Structure-aware anomaly detection via semantic prototype reconstruction in hyperbolic space.Scientific reports · 2026Article
- Calibrated Deep-Learning Risk Indexing and Latent Behavioural Profiling for Occupational Mental-Health Risk Assessment.Bioengineering (Basel, Switzerland) · 2026Article
- Illicit Bitcoin transaction detection via feature-gated temporal graph learning.Scientific reports · 2026Article
- Efficient industrial point cloud anomaly detection via spatial context aggregation and selective anomalous feature generation.Scientific reports · 2026Article
- FD-YOLO-Skin: frequency-domain enhanced YOLO for single-class skin lesion detection.Frontiers in medicine · 2026Article
- PINN-FFD: physics- and frequency-informed one-stage detector for brain tumor detection in MRI.Frontiers in oncology · 2026Article
- Article
- BAR-UNet: a boundary-aware and appearance-robust framework for polyp segmentation.Frontiers in oncology · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The gearbox, as a key transmission device in the industrial field, may lead to severe vibrations or even failures when abnormalities occur. Therefore, with the increasing complexity of industrial automation, precise anomaly localization has become crucial. To address this issue, a gearbox condition monitoring method based on an unsupervised deep convolutional support generative adversarial network (DCSGAN) is proposed. First, high-dimensional data collected is used to train the generator, and samples are generated by the generator to calculate their reconstruction errors. Next, these reconstruction errors are utilized to train one-class support vector machine (OCSVM). During the testing phase, reconstruction errors are similarly calculated for the test data, and after being normalized using the same process as the training data, the errors are input into the trained OCSVM model for anomaly detection. The proposed method has been validated on a real gearbox dataset, and experimental results indicate that the DCSGAN outperforms other models in anomaly detection. The GitHub code for the proposed DCSGAN has been made public at: https://github.com/MR-ach/DCSGAN .
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