mlsanityData to 14 Oct 2026

Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN

arXiv 2206.12076 ↗First posted 24 Jun 2022cs.LGcited by 8

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Abstract (arXiv)

Bearings are one of the vital components of rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring is essential for reducing operational costs and downtime in numerous industries. In various production conditions, bearings can be operated under a range of loads and speeds, which causes different vibration patterns associated with each fault type. Normal data is ample as systems usually work in desired conditions. On the other hand, fault data is rare, and in many conditions, there is no data recorded for the fault classes. Accessing fault data is crucial for developing data-driven fault diagnosis tools that can improve both the performance and safety of operations. To this end, a novel algorithm based on Conditional Generative Adversarial Networks (CGANs) is introduced. Trained on the normal and fault data on any actual fault conditions, this algorithm generates fault data from normal data of target conditions. The proposed method is validated on a real-world bearing dataset, and fault data are generated for different conditions. Several state-of-the-art classifiers and visualization models are implemented to evaluate the quality of the synthesized data. The results demonstrate the efficacy of the proposed algorithm.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 8 of its 58 references that are in the map. First posted on arXiv 24 Jun 2022.

Builds on

8 of 58 references.

References · 8

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  4. 2014Conditional Generative Adversarial Netscited by 2,162
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  6. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  7. 2019Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networkscited by 103
  8. 2022Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinerycited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 1; 2026: 1.

8 papers.

  1. 2023Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimizationcited by 8
  2. 2023Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directionscited by 8
  3. 2023Constrained Adversarial Learning for Automated Software Testing: a literature reviewcited by 6
  4. 2023Anomaly Detection in Automated Fibre Placement: Learning with Data Limitationscited by 3
  5. 2024Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategiescited by 3
  6. 2024Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Frameworkcited by 1
  7. 2025Feature-Weighted MMD-CORAL for Domain Adaptation in Power Transformer Fault Diagnosiscited by 1
  8. 2026Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methodscited by 0