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As a key equipment in the underground coal mining transportation system, the scraper conveyor's bearings operate for extended periods under extreme conditions such as high temperature, high impact loads, dust pollution, and continuous operation, resulting in a high risk of failure. Existing fault diagnosis methods still face numerous challenges in feature extraction and classification accuracy when dealing with complex working conditions and multiple failure modes. To address this, a bearing fault diagnosis method based on a dual-branch convolutional neural network was proposed. Firstly, one-dimensional vibration signals were converted into two-dimensional time-frequency images using continuous wavelet transform to enhance the time-frequency feature representation capability of the input data. Subsequently, a dual-branch convolutional neural network architecture was constructed, in which one branch employed a dilated convolutional residual module to strengthen local feature extraction, while the other branch introduced a global attention mechanism to optimize global feature learning, and feature fusion was applied to improve the model's diagnostic capability. Finally, experimental validation was conducted on the CWRU and PU bearing datasets. The results show that the proposed method can achieve fault diagnosis accuracy rates of 99.78% and 97.94% on the two datasets, respectively, and outperforms comparison models such as DCRB-CNN, GAM-CNN and GAM-ResNet in overall performance, demonstrating significant diagnostic capability and strong generalization ability. Moreover, bearing fault diagnosis experiments were carried out using real-world coal mine field data, further validating the method's practicality and potential for engineering deployment under strong noise and non-stationary operating conditions. The research results provide an efficient and reliable deep learning solution for the intelligent fault diagnosis of complex coal mining equipment such as scraper conveyors.

, authors=Yabing Liu, authorsList=Yabing Liu, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321893307324678884, articleId=1321893306737476322, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于双分支卷积神经网络的刮板输送机轴承故障诊断方法, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

矿用刮板输送机作为煤矿井下运输系统的关键设备，其轴承长期处于高温、高冲击载荷、粉尘污染及持续运行等极端工况下，故障风险较高。面对复杂工况与多种故障模式，现有故障诊断方法在特征提取和分类准确性方面仍面临诸多挑战。为此，提出了一种基于双分支卷积神经网络的轴承故障诊断方法。首先，利用连续小波变换将一维振动信号转换为二维时频图，以增强输入数据的时频特征表达能力。随后，构建双分支卷积神经网络架构，其中一个分支采用空洞卷积残差模块加强局部特征提取，另一个分支引入全局注意力机制优化全局特征学习，通过特征融合提升模型的诊断能力。最后，在CWRU和PU轴承数据集上进行了试验验证。结果表明，该方法在两个数据集上的故障诊断准确率分别达到99.78%和97.94%，整体性能优于DCRB-CNN、GAM-CNN和GAM-ResNet等对比模型，展现出显著的诊断能力和良好的泛化能力。此外，基于煤矿现场数据开展了轴承故障诊断试验，进一步验证了所提方法在强噪声、非平稳工况下的实用性和工程部署潜力。研究结果为刮板输送机等复杂煤矿设备的智能故障诊断提供了一种高效可靠的深度学习解决方案。

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刘亚兵（1973—），男，山西祁县人，高级工程师，主要从事煤矿机电技术管理和工业智能化研究。E-mail:772108214@qq.com

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刘亚兵（1973—），男，山西祁县人，高级工程师，主要从事煤矿机电技术管理和工业智能化研究。E-mail:772108214@qq.com

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