机电工程技术2024，Vol.53Issue(5)：207-210,4.DOI:10.3969/j.issn.1009-9492.2024.05.045
数据驱动下基于改进生成对抗网络的滚动轴承故障诊断方法
Rolling Bearing Fault Diagnosis Method Based on Improved Generative Adversarial Network Driven by Data
摘要
Abstract
To avoid the impact of rolling bearing faults on the normal operation of large wind turbines,research is conducted on the diagnosis and analysis of rolling bearing faults in wind turbines.Considering the complex feature extraction of traditional wind turbine rolling fault diagnosis,which can only distinguish true and false,and cannot identify fault types,a rolling bearing fault diagnosis method based on improved generative adversarial network for fault data is proposed.On the basis of explaining the working principle of traditional generative adversarial network,the JS divergence in the traditional generative adversarial network is replaced by the Wasserstein distance,forming the Wasseratein GAN-GP.Based on this,a rolling bearing fault diagnosis model is constructed,and the diagnosis model is optimized using gradient penalty method to solve the problem of gradient vanishing during model training.On the basis of learning the distribution of raw data,the training sample set is further expanded.Finally,experiments are conducted under the condition of imbalanced data,and the results show that the proposed rolling bearing fault diagnosis method is practical and effective,which can effectively reduce the impact of imbalanced and insufficient training data on fault diagnosis.关键词
故障数据/滚动轴承/生成对抗网络/故障诊断Key words
fault data/rolling bearings/generative adversarial network/fault diagnosis分类
机械制造引用本文复制引用
颜毅斌,管俊杰,吉天平..数据驱动下基于改进生成对抗网络的滚动轴承故障诊断方法[J].机电工程技术,2024,53(5):207-210,4.基金项目
湖南省自然科学基金资助项目(2022JJ60074) （2022JJ60074）
湖南省教育厅资助科研项目(22C1118) （22C1118）
