时变转速下基于双阈值注意力生成对抗网络和小样本的转子-轴承系统故障诊断

Fault Diagnosis of Rotor-bearing System under Time-varying Speeds by Using Dual-threshold Attention-embedded GAN and Small Samples

可用故障数据的匮乏给时变转速下转子-轴承系统的端到端故障诊断带来严重挑战,生成对抗网络为解决小样本故障诊断问题提供新思路,但其仍存在梯度消失、全局关联特征学习能力较弱和训练效率较低等缺点。因此,提出一种双阈值注意力生成对抗网络,用于生成高质量的红外热成像图片,以解决时变转速下转子-轴承系统的小样本故障诊断难题。首先,结合Wasserstein距离和梯度惩罚设计新型对抗损失函数,避免训练过程中的梯度消失。其次,构建注意力嵌入的生成对抗网络以指导学习红外热成像图片的全局热力关联特征。最后,开发双阈值训练机制进一步提高生成样本质量和训练效率。将所提方法用于分析转子-轴承系统的实测红外热成像图片,结果表明,所提方法能辅助准确诊断时变转速及小样本下的不同故障模式,性能优于目前常用的生成对抗网络方法。

End-to-end fault diagnosis of rotor-bearing system under time-varying speeds using a few samples is challenging.Despite generative adversarial network(GAN)provides a way to solve the problem of small-sample fault diagnosis,it still has some limitations,such as gradient vanishing,weak extraction of global correlation features,and low training efficiency.Therefore,a dual-threshold attention-embedded GAN is proposed for generating high-quality infrared thermal(IRT)images to solve small-sample fault diagnosis of rotor-bearing system under time-varying speeds.First,Wasserstein distance and gradient penalty are combined to design the new adversarial loss function to avoid gradient vanishing.Second,attention-embedded GAN is constructed to guide learn global thermal-correlation features of the IRT images.Finally,dual-threshold training mechanism is developed to further improve the generation quality and training efficiency.The proposed method is used to analyze the collected small IRT images of a rotor-bearing system,and the results show that the proposed method can accurately diagnosis different fault modes using small samples under time-varying speeds,which is superior to other popular GANs.