国外文献 > 1.轴承 > 7. 智能方法与计算 > 神经网络
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| 标 题： |
A Fault Diagnosis Method of Rolling Bearing of CNC Machine Tool Based on Improved Convolutional Neural Network
基于改进卷积神经网络的数控机床滚动轴承故障诊断方法 |
| 作 者： |
Ying Gao
Xiaojun Xia |
| 关键词： |
Rolling bearing [滚动轴承]
Convolutional neural network [ 卷积神经网络]
Fault diagnosis [ 故障诊断]
Deep learning [ 深度学习]
CNC machine tool [ 数控机床] |
| 出版年： |
2022 |
| 文集名： |
11th International Conference of Information and Communication Technology (ICTech 2022)
第11届信息与通信技术国际会议（ICTech 2022） |
| 会议名： |
11th International Conference of Information and Communication Technology (ICTech 2022)
第11届信息与通信技术国际会议（ICTech 2022） |
| 届 次： |
11th |
| 摘 要： |
In the industrial production process, the rolling bearing failures of huge mechanical equipment such as CNC machine tools frequently occur, which seriously affects the production performance and service life of the machine tools. In order to identify the types of faults in rolling bearings and improve the safety of the equipment, this paper presents a fault diagnosis method on account of an improved Convolution Neural Network (CNN). The improved CNN model is to add a convolutional layer before the fully connected layer, after several convolutional layers and several pooling layers, and use an improved stochastic gradient descent training algorithm with momentum to speed up the training speed to enhance the serviceability of the model. Traditional fault diagnosis methods are time-consuming, high in labor costs and low in work efficiency. The method in this paper improves the intelligence of the rolling bearing of CNC machine tools fault diagnosis process, improves the correctness of fault diagnosis, and adapts to the characteristics of big data fault diagnosis. Finally, the data set of Case Western Reserve University's rolling bearing database is used for experimental verification. The experimental results reveal that this method has a high recognition accuracy rate for various types and severity of rolling bearing faults, and has good practicability and application prospect.
在工业生产过程中，数控机床等大型机械设备的滚动轴承故障频繁发生，严重影响了设备的生产性能和使用寿命。为了识别滚动轴承的故障类型并提高设备安全性，本文提出了一种基于改进卷积神经网络（CNN）的故障诊断方法。改进后的CNN模型在全连接层之前增加了一个卷积层，在经过若干卷积层和池化层后，采用改进的动量随机梯度下降训练算法，以加快训练速度，提升模型的可靠性。传统故障诊断方法耗时长、人力成本高且工作效率低。本文的方法提高了数控机床滚动轴承故障诊断过程的智能化水平，提升了故障诊断的准确性，并适应了大数据故障诊断的特点。最后，利用西大大学滚动轴承数据库的数据集进行实验验证。实验结果表明，该方法对各类及不同程度的滚动轴承故障具有较高的识别准确率，且具有良好的实用性与应用前景。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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