上海交通大学学报

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2023 , Vol. 57 >Issue 11: 1512 - 1521

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.008

机械与动力工程

基于对不平衡数据集进行二次迁移学习的滚动轴承剥落类故障诊断方法

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  • 兰州理工大学 机电工程学院, 兰州 730050
郭俊锋(1978-),教授,主要从事现代测试与故障诊断技术研究.

收稿日期: 2022-01-20

  修回日期: 2022-03-02

  录用日期: 2022-03-14

  网络出版日期: 2023-12-01

基金资助

国家自然科学基金资助项目(51465034)

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Fault Diagnosis of Rolling Bearing with Roller Spalling Based on Two-Step Transfer Learning on Unbalanced Dataset

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  • School of Mechanical and Electronic Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Received date: 2022-01-20

  Revised date: 2022-03-02

  Accepted date: 2022-03-14

  Online published: 2023-12-01

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摘要

滚动轴承在运行过程中正常工作状态时间长,故障时间很短,导致数据集不平衡,从而极大地影响深度学习模型故障诊断的准确率.针对该问题,提出一种基于二次迁移学习的滚动轴承不平衡数据集故障诊断方法.首先使用源域和目标域中的少量数据通过条件梯度惩罚生成对抗网络(CWGAN-GP)生成过渡数据集,然后将搭建好的卷积神经网络模型在源域数据集、过渡数据集和目标域数据集之间进行两次迁移,最后使用目标域的少量数据对迁移后的模型进行微调,得到最终的故障诊断模型.实验结果表明,该方法对不同工况下数据集不平衡的滚动轴承剥落类故障有较好的诊断识别效果.

关键词： 迁移学习; 故障诊断; 不平衡数据集; 生成对抗网络

本文引用格式

郭俊锋, 王淼生, 王智明 . 基于对不平衡数据集进行二次迁移学习的滚动轴承剥落类故障诊断方法[J]. 上海交通大学学报, 2023 , 57(11) : 1512 -1521 . DOI: 10.16183/j.cnki.jsjtu.2022.008

Abstract

Under operating conditions, bearings have a substantial service life with short failure time periods, which leads to unbalanced dataset and greatly affects the accuracy of deep learning model fault diagnosis. To address this problem, a fault diagnosis method of rolling bearing unbalanced dataset based on two-step transfer learning is proposed in this paper. First, a small amount of data in the source and target domains is used to generate the transition dataset by conditional gradient penalized generative adversarial network (CWGAN-GP). Then, the constructed convolutional neural network model is migrated twice between the source domain dataset, the transition dataset, and the target domain dataset. Finally, a small amount of data from the target domain is used to fine-tune the transferred model to obtain the final fault diagnosis model. The experimental results show that the method has a good diagnostic recognition effect on rolling bearing spalling class faults with unbalanced dataset under different working conditions.

Key words： transfer learning; fault diagnosis; unbalanced dataset; generative adversarial network

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