基于改进GAN算法的电机轴承故障诊断方法

提出一种基于改进GAN(生成对抗网络)的滚动轴承故障诊断方法，以振动信号作为主要依据，结合连续小波变换处理非平稳信号的能力和半监督生成对抗网络(semi-supervised generation adversarial networks，SSGAN)处理和识别图像的功能，在半监督生成对抗网络的基础上引入条件模型并对损失函数进行优化，指导生成器和判别器的训练.首次将改进GAN算法应用于故障诊断领域并利用其生成模型和半监督学习能力分别解决了样本数据不足和样本标记问题.实验表明，连续小波变换与改进GAN 结合的故障诊断方法与其他主流诊断方法相比能达到较高准确率.

Fault Diagnosis Method of Motor Bearing Based on Improved GAN Algorithm

A fault diagnosis method was proposed for rolling bearing based on improved generation adversarial networks(GAN). Taking the vibration signal as the main basis， combined with the ability of continuous wavelet transform to process non-stationary signals and the functions of semi-supervised generation adversarial networks (SSGAN) processing and image recognition for fault diagnosis， the condition model was introduced based on semi-supervised generation adversarial networks， and the loss function was optimized to guide the generator and discriminator. For the first time， the improved GAN algorithm was applied to the field of fault diagnosis and its generation model and semi-supervised learning ability were used to solve the problem of sample data shortage and sample labeling. Experimental results showed that the fault diagnosis method combining continuous wavelet transform and improved GAN can achieve higher accuracy than that of the other mainstream diagnostic methods.