| 基于深度卷积神经网络的轴承多故障诊断研究 |
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| 引用本文： | 刘慧斌,李少波,张安思,杨万里,黄金苑,姚勇.基于深度卷积神经网络的轴承多故障诊断研究[J].组合机床与自动化加工技术,2020(5):12-16. |
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| 作者姓名： | 刘慧斌 李少波 张安思 杨万里 黄金苑 姚勇 |
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| 作者单位： | 贵州大学现代制造技术教育部重点实验室;贵州大学机械工程学院 |
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| 基金项目： | 国家智能制造新模式应用项目(工信厅装函[2017]468号,工信部联装[2016]213号);贵州省科技计划项目(黔科合人才[2015]4011,黔科合平台人才[2016]5103,黔科合平台人才[2017]5788)。 |
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| 摘 要： | 针对现有研究轴承单一故障较多而研究复杂多故障较少的不足,结合卷积神经网络自动提取特征的特性,文章提出较为先进的无需人工提取故障特征的端到端深度卷积神经网络方法进行轴承多故障诊断。与基于人工提取故障特征的神经网络故障诊断方法相比较,该方法提高了轴承多故障诊断的精度,并有效区分故障发生位置,可为工业应用提供可靠的理论实验依据。
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| 关 键 词： | 深度学习 卷积神经网络 多故障诊断 |
Multiple Damage Diagnosis of Bearings Based on Deep Convolutional Neural Network |
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| LIU Hui-bin,LI Shao-bo,ZHANG An-si,YANG Wan-li,HUANG Jin-yuan,YAO Yong.Multiple Damage Diagnosis of Bearings Based on Deep Convolutional Neural Network[J].Modular Machine Tool & Automatic Manufacturing Technique,2020(5):12-16. |
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| Authors: | LIU Hui-bin LI Shao-bo ZHANG An-si YANG Wan-li HUANG Jin-yuan YAO Yong |
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| Affiliation: | (Key Laboratory of Advanced Manufacturing Technology,Ministry of Education,Guizhou University,Guiyang 550025,China;School of Mechanical Engineering,Guizhou University,Guiyang 550025,China) |
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| Abstract: | In view of the fact that there are many single damage in the existing research and less in multiple damage of bearings,combined with the characteristics of automatic extraction of features by convolutional neural networks,the paper proposes an end-to-end deep convolutional neural network method,which does not require manual extraction of fault features for bearing fault diagnosis.Compared with the neural network fault diagnosis method based on manual extraction fault feature,the method improves the accuracy of bearing fault diagnosis and effectively distinguishes the fault location,which can provide a reliable theoretical experimental basis for industrial applications. |
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| Keywords: | deep learning convolutional neural network multiple diagnosis |
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