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Open Access

ARTICLE

Rolling Bearing Fault Detection Based on Self-Adaptive Wasserstein Dual Generative Adversarial Networks and Feature Fusion under Small Sample Conditions

Qiang Ma1,2,3,4,5, Zhuopei Wei1,2, Kai Yang1,2,*, Long Tian1,2, Zepeng Li1,2
1 School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, 056038, China
2 Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan, 056038, China
3 Department of Mechanics, Tianjin University, Tianjin, 300354, China
4 Tianjin Key Laboratory of Nonlinear Dynamics and Control, Tianjin, 300354, China
5 National Demonstration Center for Experimental Mechanics Education, Tianjin University, Tianjin, 300354, China
* Corresponding Author: Kai Yang. Email: email

Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2025.060596

Received 05 November 2024; Accepted 13 February 2025; Published online 19 March 2025

Abstract

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.

Keywords

Deep learning; Wasserstein deep convolutional generative adversarial network; small sample learning; feature fusion; multidimensional data enhancement; small sample fault diagnosis

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