首页|基于多判别器辅助分类器生成对抗网络的故障诊断方法研究

基于多判别器辅助分类器生成对抗网络的故障诊断方法研究

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
在强冲击、强辐射、极高温等极端恶劣的工作环境下,机械设备的故障模式复杂多样,获得充足且有效的故障数据变得非常困难甚至难以实现,以致故障诊断的准确性受限,后续检修维护方案难以有效制定.针对这一问题,提出了一种多判别器辅助分类器生成对抗网络的数据增强算法.通过设置3个判别器、1个生成器并添加独立的分类器,构建了新的辅助分类器生成对抗网络模型.针对在该模型训练中存在的不稳定性问题,通过引入Wasserstein距离构造新的损失函数,并采用稳定性更具优势的单边软约束正则化项替换原有的L2梯度惩罚项来解决模型崩溃问题;在此基础上,采用高效通道注意力机制来进一步提高模型的特征提取能力.将所提出的模型应用于扩充机械设备故障数据集,辅助深度学习智能诊断模型的训练.多个故障数据集扩充实验表明,与现有模型相比,新模型所生成数据的质量更高,故障诊断的准确率也得到进一步提高,因此具有较高的应用价值.
Research on fault diagnosis method based on multi-discriminator auxiliary classifier generative adversarial network
In extremely harsh working environments such as strong impacts,intense radiation and extremely high temperature,the fault modes of mechanical equipment are complex and varied,and it is very difficult to obtain sufficient and effective fault data,even difficult to achieve,so that the accuracy of fault diagnosis is limited,and subsequent maintenance and repair programs are difficult to be effectively developed.To solve this problem,a data enhancement algorithm for multi-discriminator auxiliary classifier generative adversarial network was proposed.By setting up 3 discriminators,1 generator and adding independent classifier,a new auxiliary classifier generative adversarial network model was constructed.Aiming at the instability issue in the model's training,the Wasserstein distance was introduced to construct a new loss function,and the unilateral soft constraint regularization term with more stability was used to replace the original L2 gradient penalty term to solve the problem of model collapse.Building on this,an efficient channel attention mechanism was adopted to further improve the model's feature extraction capability.The proposed model was applied to extend the fault data set of mechanical equipment to assist the training of deep learning intelligent diagnosis model.Multiple fault data set expansion experiments showed that compared with the existing model,the new model could generate higher quality data,and the accuracy of fault diagnosis was further improved,so it had high application value.

multi-discriminator auxiliary classifier generative adversarial networkefficient channel attention mechanismLipschitz penaltydata augmentationfault diagnosis

叶子汉、王中华、姜潮、吕新、张哲

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湖南大学 整车先进设计制造技术全国重点实验室,湖南 长沙 410082

湖南大学机械与运载工程学院,湖南长沙 410082

多判别器辅助分类器生成对抗网络 高效通道注意力机制 Lipschitz(利普希茨)约束 数据增强 故障诊断

国防基础科研计划资助项目国家自然科学基金资助项目整车先进设计制造技术全国重点实验室开放基金资助项目

JCKY2020110C1055220526232175001

2024

10.3785/j.issn.1006-754X.2024.03.212

工程设计学报
浙江大学 中国机械工程学会

工程设计学报

CSTPCD北大核心
影响因子：0.694
ISSN：1006-754X
年,卷(期)：2024.31(2)
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