Rolling Bearing Fault Detection Based on Self-Adaptive Wasserstein Dual Generative Adversarial Networks and Feature Fusion under Small Sample Conditions
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, ... | Find, read and cite all the research you need on Tech Science Press
Authors
- Zhuopei Wei
- Qiang Ma (ORCID: https://orcid.org/0000-0002-2249-7180)
- Zepeng Li (ORCID: https://orcid.org/0000-0003-2999-9615)
- Kai Yang (ORCID: https://orcid.org/0000-0002-5260-571X)
- Long Tian (ORCID: https://orcid.org/0000-0001-7591-4294)
Publication Details
- Journal
- Structural durability & health monitoring
- Published
- 2025-01-01
- DOI
- https://doi.org/10.32604/sdhm.2025.060596
- Citations
- 3
- Primary Topic
- Gear and Bearing Dynamics Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.14
Funders
- National Natural Science Foundation of China