基于WGAN-div和DLA34模型的轴承故障智能诊断新方法  

A New Method of Bearing Fault Intelligent Diagnosis Based on WGAN-div and DLA34 Models

作　　者：李靖宇 董增寿[1] 康琳[1] 石慧[1] LI Jingyu;DONG Zengshou;KANG Lin;SHI Hui(School of Electronic Information Engineering,Taiyuan University of Science and Technology,Taiyuan Shanxi 030024,China)

机构地区：[1]太原科技大学电子信息工程学院,山西太原030024

出　　处：《机床与液压》2024年第19期225-236,共12页Machine Tool & Hydraulics

基　　金：山西省基础研究计划(自由探索类)面上项目(20210302123206,202203021211205,202203021222214);山西省回国留学人员科研资助项目(2021-135,2021-134)。

摘　　要：针对实际工业场景中设备长时间运行于正常运行状态,故障样本不易获得且采集到的样本种类不平衡,导致以数据为驱动的深度智能诊断模型性能退化的问题,提出一种基于生成对抗网络(WGAN-div)和深度卷积神经网络DLA的两阶段处理模型。利用WGAN-div生成故障样本,实现样本间的类平衡,将平衡后的数据集送入DLA34网络中进行特征提取和故障分类。DLA34以其特殊的聚合结构能够融合各层的语义和空间信息,实现更深的信息共享。最后,利用凯斯西储大学轴承故障数据集进行验证。实验结果表明:该模型中WGAN-div能生成与原始样本高度相似的生成样本,数据平衡效果也优于目前主流的GAN、WGAN和DCGAN;且由DLA34完成的故障识别准确率在所设数据集上均达到100%。Aiming at the problem that the equipment runs in normal operation state for a long time in actual industrial scenarios,the fault samples are not easy to obtain and the types of samples are not balanced,which lead to performance degradation of data-driven deep intelligent diagnostic model,a two-stage processing model based on Wasserstein-divergence generative adversarial networks(WGAN-div) and deep convolutional neural networks DLA was proposed.WGAN-div was used to generate fault samples to achieve class balance among samples,and the balanced data set was fed into DLA34 network for feature extraction and fault classification.DLA34 could integrate semantic and spatial information of each layer with its special aggregation structure to achieve deeper information sharing.Finally,the bearing failure dataset of Case Western Reserve University was used for verification.The experimental results show that WGAN-div in the proposed model can generate samples that are highly similar to the original samples,and the data balancing effect is better than that of the current mainstream GAN,WGAN and DCGAN.The accuracy of fault recognition completed by DLA34 can reach 100% on the set of data sets.

关 键 词：故障诊断 样本类平衡 WGAN-div DLA34 

分 类 号：TH133.33[机械工程—机械制造及自动化]