| 张文风， 周俊.基于Dropout CNN的滚动轴承故障诊断研究[J].轻工机械,2019,37(2):62-67 |
| 基于Dropout CNN的滚动轴承故障诊断研究 |
| Fault Diagnosis Method of Rolling Bearing Based on Dropout CNN |
| |
| DOI：10.3969/j.issn.1005 2895.2019.02.012 |
| 中文关键词: 滚动轴承 故障诊断 Dropout 卷积神经网络 深度学习 振动信号 特征提取 |
| 英文关键词:rolling bearing fault diagnosis Dropout convolutional neural network deep learning vibration signal feature extraction |
| 基金项目: |
|
| 摘要点击次数: 1237 |
| 全文下载次数: 1454 |
| 中文摘要: |
| 针对滚动轴承故障特征很难提取及传统故障诊断方法准确率偏低的问题，提出一种基于Dropout的改进卷积神经网络(Dropout
CNN)结构，可以无需预先提取滚动轴承振动信号的故障特征，直接端到端的实现滚动轴承故障诊断。该方法以振动信号为监测信号，使用傅里叶变换生成振动
信号频谱图，以此作为整个系统的输入，利用卷积神经网络强大的特征提取能力可以自动完成故障特征提取以及故障识别。试验结果表明该方法平均诊断准确率
高达99.5%。该方法实现了大量样本下滚动轴承不同故障类型的故障特征自适应提取与故障状态的准确识别。 |
| 英文摘要: |
| Aiming at the difficulty of the rolling bearing fault feature extraction and the low accuracy of the traditional fault diagnosis method, an improved
convolution neural network (Dropout CNN) structure based on Dropout was proposed to realize the fault diagnosis of rolling bearing directly from end to end without extracting the fault
features of rolling bearing vibration signals in advance. The vibration signal was used as the monitoring signal and the frequency spectrum of the vibration signal was generated by Fourier
transform. The fault feature and fault identification can be automatically completed by using the powerful feature extraction capability of the convolutional neural network. The experimental
results show that the average diagnostic accuracy of this method is as high as 99.5%. It realizes adaptive fault feature extraction and fault state identification of different fault types of rolling
bearings under a great quantity of samples.
〖WT5HZ〗Keywords：〖WT5BZ〗rolling |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |
