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Rolling Bearing Fault Detection Based on Self-Adaptive Wasserstein Dual Generative Adversarial Networks and Feature Fusion under Small Sample Conditions
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Rolling Bearing Fault Detection Based on Self-Adaptive Wasserstein Dual Generative Adversarial Networks and Feature Fusion under Small Sample Conditions
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Journal Article
Rolling Bearing Fault Detection Based on Self-Adaptive Wasserstein Dual Generative Adversarial Networks and Feature Fusion under Small Sample Conditions
2025
Overview
An intelligent diagnosis method based on self-adaptive Wasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction, which are commonly faced by rolling bearings and lead to low diagnostic accuracy. Initially, dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty (1D-2DWDCGAN) are constructed to augment the original dataset. A self-adaptive loss threshold control training strategy is introduced, and establishing a self-adaptive balancing mechanism for stable model training. Subsequently, a diagnostic model based on multidimensional feature fusion is designed, wherein complex features from various dimensions are extracted, merging the original signal waveform features, structured features, and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales; thus, efficient and accurate small sample fault diagnosis is facilitated. Finally, an experiment between the bearing fault dataset of Case Western Reserve University and the fault simulation experimental platform dataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy. The diagnostic accuracy after data augmentation reached 99.94% and 99.87% in two different experimental environments, respectively. In addition, robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds, verifying its good generalization performance.
Publisher
Tech Science Press
