四川轻化工大学学报（自然科学版）2024，Vol.37Issue(4)：19-27,9.DOI:10.11863/j.suse.2024.04.03
基于SE-ResNet和迁移学习的轴承故障诊断方法研究
Research on Bearing Fault Diagnosis Method Based on SE-ResNet and Transfer Learning
摘要
Abstract
In response to the problem of low accuracy and poor generalization in traditional rolling bearing fault diagnosis under small sample conditions,a bearing fault diagnosis method based on SE-ResNet and transfer learning is proposed in this paper.Firstly,the data is preprocessed and transformed into two-dimensional images.Secondly,the attention mechanism of the Squeeze and Excitation network structure is introduced into the residual blocks of the neural network to improve the deep residual network model.Then,the transformed images are fed into the SE-ResNet model for classification of the rolling bearing states in the test samples.Finally,by using transfer learning to utilize a model trained on small samples as a pre-trained model,experiments are conducted with data from different working conditions.This approach is compared to 2D-CNN,ResNeXt-50,AlexNet,and BiLSTMmethods.The results demonstrate that the accuracy of SE-ResNet under single working conditions in CWRU datasets ranges from 87.09%to 99.61%,while the accuracy of the SE-ResNet+transfer learning method ranges from 92.8%to 99.54%and 85.1%to 100%under the working conditions of the two datasets(CWRU、XJTU-SY)in this paper,respectively.These results indicate that the SE-ResNet+transfer learning method exhibits superior accuracy and generalization performance compared to other methods.关键词
小样本/故障诊断/SENet/ResNet/迁移学习Key words
small sample/fault diagnosis/SENet/ResNet/transfer learning分类
信息技术与安全科学引用本文复制引用
唐宇峰,杨泽林,胡光忠,曹睿,何俚秋..基于SE-ResNet和迁移学习的轴承故障诊断方法研究[J].四川轻化工大学学报（自然科学版）,2024,37(4):19-27,9.基金项目
四川省科技厅科技支撑项目(2022NSFSC1154) （2022NSFSC1154）
企业信息化与物联网测控技术四川省高校重点实验室开放基金项目(2023WYJ04) （2023WYJ04）
四川轻化工大学科研创新团队计划项目(SUSE652A004) （SUSE652A004）
