期刊论文

Pan, ZF

英文

Neurocomputing

0925-2312

2025

622

129288

10.1016/j.neucom.2024.129288

CRediT authorship contribution statement Ling Li: Methodology, Formal analysis, acquisition, Writing – review & editing. Zhuofu Pan: Conceptualization, Software, Visualization, Writing – original draft, review & editing. Yucheng Ma: Investigation, Visualization, Validation, Writing – original draft & editing.. Zhiwen Chen: Methodology. Jie Chen: Methodology, Formal analysis. Yalin Wang: Supervision, acquisition, Project administration..

本校为第一机构

Thanks to the strong generalization and generation capabilities, Generative Adversarial Networks (GANs) have been developed for fault diagnosis schemes in recent years. Most of them are constructed as data enhancers for generating few-shot faulty samples and serve for the fault classification task. However, classifier-based fault diagnosis frameworks have difficulty in recognizing new fault classes and estimating fault magnitudes. In order to exploit the potential of GAN for fault detection and estimation, this paper proposes a Fault-estimable AutoEncoder-GAN (FAE-GAN), which uses a new framew...