北京航空航天大学学报2026，Vol.52Issue(3)：713-723,11.DOI:10.13700/j.bh.1001-5965.2023.0819
基于SCACGAN的小样本齿轮箱故障诊断
Fault diagnosis of gearbox with small-sample based on SCACGAN
摘要
Abstract
A new method for gearbox fault diagnosis based on the self-correcting auxiliary classifier generative adversarial networks(SCACGAN)is suggested in response to the limited diversity and low quality of fault samples produced by the auxiliary classifier generative adversarial networks(ACGAN)during the small-sample gearbox fault diagnosis process,which subsequently results in low diagnostic accuracy.Firstly,an independent classifier is introduced into the auxiliary classifier generative adversarial network to mitigate the adverse impact of discriminator output errors on the quality of generated samples,and to classify the health status of different gearbox samples.Secondly,the problem of low-quality generated samples during the training phase is addressed by using the least squares function to improve the model's generation and classification skills.Lastly,a self-correcting convolutional neural network is integrated into the generator to enhance the capability of fault feature acquisition.Experimental results demonstrate that under small-sample conditions,the proposed approach is capable of generating higher-quality fault samples,thereby improving the accuracy of gearbox fault diagnosis.关键词
齿轮箱/小样本/辅助分类器生成对抗网络/自校正卷积神经网络/故障诊断Key words
gearbox/small-sample/auxiliary classifier generative adversarial networks/self-correcting convolutional neural networks/fault diagnosis分类
信息技术与安全科学引用本文复制引用
王进花,刘秦玮,曹洁,陈莉..基于SCACGAN的小样本齿轮箱故障诊断[J].北京航空航天大学学报,2026,52(3):713-723,11.基金项目
国家自然科学基金(62063020,61763028) （62063020,61763028）
国家重点研发计划(2020YFB1713600) （2020YFB1713600）
甘肃省自然科学基金(20JR5RA463) National Natural Science Foundation of China(62063020,61763028) （20JR5RA463）
National Key Research and Development Program of China(2020YFB1713600) （2020YFB1713600）
Natural Science Foundation of Gansu Province(20JR5RA463) （20JR5RA463）
