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

arXiv: https://arxiv.org/abs/2206.12076 · HTML: https://mlsanity.com/paper/2206.12076/

## 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 (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks](https://mlsanity.com/paper/1909.09586/) (2019)
- [Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery](https://mlsanity.com/paper/2202.04212/) (2022)

## Built on by (most cited first, 8 of 8)

- [Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimization](https://mlsanity.com/paper/2307.03860/) (2023)
- [Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions](https://mlsanity.com/paper/2310.03195/) (2023)
- [Constrained Adversarial Learning for Automated Software Testing: a literature review](https://mlsanity.com/paper/2303.07546/) (2023)
- [Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations](https://mlsanity.com/paper/2307.07893/) (2023)
- [Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies](https://mlsanity.com/paper/2401.10266/) (2024)
- [Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework](https://mlsanity.com/paper/2404.05891/) (2024)
- [Feature-Weighted MMD-CORAL for Domain Adaptation in Power Transformer Fault Diagnosis](https://mlsanity.com/paper/2505.14896/) (2025)
- [Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods](https://mlsanity.com/paper/2607.19455/) (2026)

## 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.