Qiang Ma1,2,3,4,5, Zhuopei Wei1,2, Kai Yang1,2,*, Long Tian1,2, Zepeng Li1,2
Structural Durability & Health Monitoring, Vol.19, No.4, pp. 1011-1035, 2025, DOI:10.32604/sdhm.2025.060596
- 30 June 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,… More >