| 摘要: |
| 双列圆锥滚动轴承在列车走行部应用广泛，由于该类轴承结构比较复杂，传统的故障诊断方法难以识别该类轴承的早期微弱故障.为此，提出基于深度学习的双列圆锥滚动轴承早期微弱故障诊断方法.首先，对轴承的振动信号进行经验模态分解，提取信号的瞬时能量构造特征向量；最后，利用深度学习方法对特征向量进行无监督学习，生成故障诊断分类器，完成故障的分类识别.实验中对某型号双列圆锥滚动轴承的正常状态、内圈故障和外圈故障进行信号分析与故障识别.结果表明，所提方法能有效识别双列圆锥滚动轴承的早期微弱故障，分类准确率达到98%. |
| 关键词: 深度学习 双列圆锥滚动轴承 经验模态分解 故障诊断 |
| DOI： |
| 分类号:U264.8TAH133.3 |
| 基金项目:湖南科技大学机械设备健康维护湖南省重点实验室开放基金资助项目(201605)；湖南科技大学博士科研启动基金资助项目（E57101） |
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| Fault diagnosis for double row tapered roller bearingbased on deep learning method and EMD |
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Liao Ning1, Tao Jie2,Yang Dalian31,2,3
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1. Swan College, Central South University of Forestry and Technology, Changsha 410000, China;2. Key Laboratory of Knowledge Processing and Networked Manufacturing, Hunan University of Science and Technology, Xiangtan 411201, China;3. Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, China
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| Abstract: |
| The double row tapered roller bearing is widely used in urban rail transit, due to its complex structure, the traditional fault diagnosis is difficult to recognize the early weak fault. In order to solve this problem, a deep learning method for fault diagnosis of double row tapered roller bearing was put forward. In the experiment, the bearing vibration signals were firstly separated into a series of intrinsic mode functions by empirical mode decomposition, then the transient energy was extracted to construct the eigenvectors. In the pattern recognition, deep learning method was used to generate the fault diagnosis classifier by unsupervised study with eigenvectors. There were three states of rolling bearings in experiments, as normal, inner fault and outer fault. The results show that the proposed method is more stable and accurately to identify bearing faults, and the classification accuracy is 98%. |
| Key words: deep learning double row tapered roller bearing empirical mode decomposition fault diagnosis |
