噪声与振动控制2026，Vol.46Issue(2)：95-101,7.DOI:10.3969/j.issn.1006-1355.2026.02.015
基于振动灰度图的DCGAN结合ResNet50的发电机故障诊断
Generator Fault Diagnosis Based on DCGAN of Vibrational Grayscale Image Combined with ResNet50
摘要
Abstract
The fault diagnosis method based on deep learning can effectively realize the intelligent diagnosis of generator faults,but overfitting may occur easily when the training sample is too small or the sample distribution is not uniform,which affects the diagnosis effect and accuracy of the model.In order to solve these problems,this paper proposed a generator fault diagnosis method based on the deep convolutional generating adversal network(DCGAN)of vibrational grayscale image and residual network(ResNet50),which realizes the generator fault intelligent diagnosis under the condition of small fault samples and unbalanced fault samples.It was proved by experiment that the fault samples generated by DCGAN can be used to train the model in the case of small fault samples or unbalanced fault samples with higher diagnosis accuracy guaranteed.关键词
故障诊断/振动灰度图/深度卷积生成对抗网络/残差网络Key words
fault diagnosis/vibrational grayscale map/DCGAN/ResNet50分类
信息技术与安全科学引用本文复制引用
张超,李晨昕,周天赐,靳瑞卿,何玉灵..基于振动灰度图的DCGAN结合ResNet50的发电机故障诊断[J].噪声与振动控制,2026,46(2):95-101,7.基金项目
国家自然科学基金(52177042) （52177042）
河北省自然科学基金(E2022502003、E2021502038) （E2022502003、E2021502038）
中央高校基本科研业务费专项基金(2023MS128) （2023MS128）
