摘要详情

ID / 提交时间

202 / 2024-09-02 09:02:29

标题

A Novel Method for Rotating Machinery Based on Generative Adversarial Networks

关键字

Rotating,Machinery

主题及专题

Special Sessions > SS03 Advanced Sensing, Monitoring and Diagnosis of Renewable Energy batteries

状态

全文被拒

作者

储兆航 / 安徽大学

摘要

In the real scenario of engineering, the failure time of rotating machinery is generally much less than when it is in a healthy condition. Considering the cost, it is unrealistic to conduct the large-sample and long-time failure tests. This results in the problem of data imbalance in fault diagnosis, i.e., the number of normal samples far exceeds that of the fault ones, which seriously affects the accuracy and stability of fault diagnosis. For the settlement of the above problem, an auxiliary classier Wasserstein generative adversarial network with gradient penalty (ACWGAN-GP) is proposed in this article, which is capable of generating high-quality samples for the minority classes stably utilizing an imbalanced training set.