国外文献 > 1.轴承 > 7. 智能方法与计算 > 神经网络
关键词搜索：
| 标 题： |
A Fault Diagnosis Method of Rolling Bearing Based on Convolutional Neural Network
基于卷积神经网络的滚动轴承故障诊断方法 |
| 作 者： |
Bangcheng Zhang
Shuo Gao
Guanyu Hu
Zhi Gao
Yadong Zhao
Jianzhuang Du |
| 关键词： |
Rolling bearing [滚动轴承]
Convolutional neural network [ 卷积神经网络]
Fault diagnosis [ 故障诊断]
Fourier transform [ 傅里叶变换] |
| 出版年： |
2023 |
| 文集名： |
35th Chinese Control and Decision Conference (CCDC 2023)
第35届中国控制与决策会议（CCDC 2023） |
| 会议名： |
35th Chinese Control and Decision Conference (CCDC 2023)
第35届中国控制与决策会议（CCDC 2023） |
| 届 次： |
35th |
| 摘 要： |
Rolling bearing is a vital part of mechanical components, and its working environment is complex, which is prone to failure. Once a fault occurs, the rotating machinery will be shut down or even scrapped, which directly affects the economic benefits and personal safety. Therefore, this paper presents a rolling bearing fault diagnosis method based on convolution neural network (CNN). Through a series of comparative experiments, the best parameters of the model, such as learning rate, BatchSize, Dropout layer position and Dropout rate, are determined. In this article, after Fourier transform of primitive vibration signal of the bearing, the frequency domain signal is transformed into a two-dimensional condition as input of CNN to diagnose and identify inner circle fault, outer circle fault, cage fault and mixed fault. Finally, the experimental data set of rolling bearings published by Xi 'an Jiaotong University is used, the diagnostic accuracy rate is as high as 99.89%, which shows that the model can effectively diagnose rolling bearing faults. In addition, the proposed method is compared with Gates recurrent neural (GRU) and long-short term memory neural networks (LSTM), and the results show the advantages of the proposed method.
滚动轴承是机械部件中的关键组成部分，其工作环境复杂，容易发生故障。一旦出现故障，旋转设备将被迫停机甚至报废，直接影响经济效益和人身安全。因此，本文提出一种基于卷积神经网络（CNN）的滚动轴承故障诊断方法。通过一系列对比实验，确定了模型的最佳参数，如学习率、批量大小、Dropout层位置及Dropout比率。在本文中，对轴承原始振动信号进行傅里叶变换后，将频域信号转换为二维输入形式，输入至CNN进行内部圆环故障、外部圆环故障、保持架故障以及混合型故障的识别与诊断。最后，采用西安交通大学发布的滚动轴承实验数据集进行验证，诊断准确率达到99.89%，表明该模型能够有效诊断滚动轴承故障。此外，所提出的方法与门控循环神经网络（GRU）和长短期记忆神经网络（LSTM）进行了比较，结果表明了该方法的优势。 |
| 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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