| 高洁,祝洪宇,贾朱植,宋向金.基于改进MACNN-BiGRU的跨工况轴承故障分类方法研究[J].电子测量与仪器学报,2025,39(12):77-90 |
| 基于改进MACNN-BiGRU的跨工况轴承故障分类方法研究 |
| Across working conditions fault classification method forrolling bearing based on improved MACNN-BiGRU |
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| DOI： |
| 中文关键词: 轴承故障诊断 瓶颈模块 双向门控循环单元 子领域自适应 局部最大均值差异 |
| 英文关键词:bearing fault diagnosis bottleneck module bidirectional gated recurrent unit subdomain adaptation local maximum mean discrepancy |
| 基金项目:国家自然科学基金青年基金项目（项目编号：52007078）、辽宁省教育厅基本科研项目（JYTMS20230946） |
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| Author | Institution |
| Gao Jie | School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China |
| Zhu Hongyu | School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China |
| Jia Zhuzhi | School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China |
| Song Xiangjin | School of Electrical Information Engineering, Jiangsu University, Zhenjiang 212013, China |
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| 中文摘要: |
| 针对电机跨工况运行时滚动轴承故障特征分布差异和有用信息偏少导致诊断准确率低的问题，提出了改进多通道卷积双向门控模型(IMACNN-BiGRU)
的跨工况滚动轴承故障诊断方法。设计了带有瓶颈模块和BiGRU模块的多通道卷积神经网络模型，该模型使用多通道结构端对端地捕捉原始振动信号中的全局故障信息，借助瓶颈模块减轻模型计算负担，利用BiGRU模块优化信息传递路径，采用局部最大均值差异完成子领域适配，有效缩减源域和目标域在预训练模型中的特征分布差异。区分不同负载相同转速、不同负载不同转速和大跨度工况变化3种情况，在SDUST 、CWRU、PU公开轴承数据集上分别设计了12个迁移任务对所提方法进行实验验证。结果表明，所提方法的故障分类平均准确率分别达到90.09%、99.70%、91.75%，明显高于最大均值差异（MMD）、域对抗神经网络（DANN）、条件对抗网络（CDAN）等对比方法，在强工况偏移条件下，该方法仍然保持了最高99.99%的单任务精度和最小波动，兼具高准确性与强泛化能力。在CWRU数据集上的实验结果对比进一步表明，所提方法的跨工况轴承故障分类准确率优于DAMSCN-BiGRU、MSDAM和改进DANN的无监督域适应网络模型。 |
| 英文摘要: |
| Aiming at the bad fault classification accuracy of rolling bearing in motor due to different probability distributions and insufficient sample data, a fault classification method is proposed based on improved multi-channel convolutional network with bidirectional gated units for cross-working conditions. A multi-channel convolutional neural network with bottleneck and BiGRU module was designed to capture global fault information from raw vibration signals end-to-end, while reducing computational load through the bottleneck module and optimizing information flow through the BiGRU module. Local maximum mean discrepancy (LMMD) is adopted to complete subdomain adaptation, reducing the feature distribution differences between the source and target domains in the pre-trained models. Three scenarios were distinguished: different loads at the same speed, different loads at different speeds, and conditions with a wide range of variations. Twelve transfer tasks were designed on the SDUST, CWRU, and PU public datasets to experimentally validate the proposed method. The experimental results show that the average accuracy of the proposed method reaches 90.09%, 99.70%, and 91.75% respectively, significantly higher than comparison methods such as MMD, DANN, and CDAN. It maintains a top single-task accuracy of 99.99% under strong scenario shifts, showing high precision and generalization. The results of CWRU dataset show that, compared with other methods, such as DAMSCN-BiGRU, MSDAM, and improved DANN unsupervised domain adaptation model, the proposed method has also a higher accuracy quantity under cross-working conditions. |
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