摘要
近些年,数据增强算法被广泛应用于小样本故障分类中。然而,传统的数据增强模型在训练中经常出现梯度爆炸、梯度消失等问题,这在一定程度上限制了其在滚动轴承故障分类上的应用。为了解决上述问题,提出了一种新的模型框架。该模型首先将滚动轴承的原始一维振动数据通过连续小波变换(CWT)转换为二维图像,然后利用变分自动编码生成式对抗网络(VAE-GAN)对图像数据做样本增强,最后利用生成图片和原图片共同训练一个卷积神经网络(CNN)故障分类器。使用凯斯西储大学实验室的公开数据集对所提出的方法进行了验证。实验结果表明,与其他模型相比,所提出的模型具有更优越的性能。
At present,the rolling bearing fault diagnosis method in the nuclear power industry is mainly based on inspection and human analysis and judgment,and the effective sample data of bearing fault in the actual industrial process is relatively scarce,which limits the application of data-based intelligent models such as deep learning.Therefore,in the nuclear industry,the research on few-shot fault diagnosis is of great significance.In recent years,data augmentation algorithms have been widely used in small sample fault classification and achieved good results.However,the traditional data augmentation model often has problems such as gradient explosion and gradient disappearance during training,resulting in poor image quality and low fault diagnosis accuracy,which limits its application in rolling bearing fault classification to a certain extent.To address above issues,a new model framework was proposed.The model first converted the original one-dimensional vibration data of the rolling bearing into a two-dimensional image through continuous wavelet transform(CWT).Then the variational autoencoder-generative adversarial network(VAE-GAN)was uesd to do sample enhancement.Finally,the generated image and the original image were used to jointly train a convolutional neural network(CNN)fault classifier.By introducing the CWT method,the one-dimensional time-frequency image is converted into a two-dimensional image,which can not only give full play to the powerful feature representation ability of convolutional neural networks,but also enable the classification network to better extract data features,and can also directly observe when the data is generated.The quality of generated images facilitates the improvement of network models.The introduction of VAE-GAN greatly improves the stability,diversity,and clarity of the images generated by the entire model compared with traditional data enhancement methods.And the using of the convolutional autoencoder makes it work better at image generation.The proposed method is validated usi
作者
张钊光
蒋庆磊
詹瑜滨
侯修群
郑英
崔运佳
ZHANG Zhaoguang;JIANG Qinglei;ZHAN Yubin;HOU Xiuqun;ZHENG Ying;CUI Yunjia(Research Institute of Nuclear Power Operation,Wuhan 430200,China;China Nuclear Power Operation Technology Corporation,Ltd.,Wuhan 430200,China;CNNP Guodian Zhangzhou Energy Co.,Ltd.,Zhangzhou 363300,China;China-belt and Road Joint Laboratory on Measurement and Control Technology,School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《原子能科学技术》
EI
CAS
CSCD
北大核心
2023年第S01期228-237,共10页
Atomic Energy Science and Technology
关键词
小样本
滚动轴承
故障诊断
连续小波变换
变分自动编码生成式对抗网络
卷积神经网络
few-shot
rolling bearing
fault diagnosis
continuous wavelet transform
variational autoencoder-generative adversarial network
convolutional neural network
