| 改进一维卷积神经网络与双向门控循环单元的轴承故障诊断研究 |
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| 引用本文： | 杨云,丁磊,张昊宇.改进一维卷积神经网络与双向门控循环单元的轴承故障诊断研究[J].机械科学与技术(西安),2023,42(4):538-545. |
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| 作者姓名： | 杨云 丁磊 张昊宇 |
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| 作者单位： | 华东交通大学 电气与自动化工程学院, 南昌 330033 |
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| 基金项目： | 江西省重点研发计划项目20202BBEL53008 |
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| 摘 要： | 针对传统智能故障诊断依赖于人工经验进行特征提取和传统卷积神经网络(Convolutional neural networks, CNN)参数过多、训练量过大且无法充分利用时间序列信息的缺点，提出一种基于改进一维卷积神经网络与双向门控循环单元的深度学习新算法。首先，该方法利用一维卷积神经网络自提取能力进行特征提取，同时设计了一个全局均值池化层替换传统卷积神经网络的全连接层，减少参数数量；其次，引入双向门控循环单元学习特征信号中的时间序列关系；最后，通过支持向量机替换传统CNN中的Softmax层进行故障分类，进一步提高诊断的准确率。实验表明，该方法将诊断的准确率提升至99.8%,并且加快了诊断的速度。通过与其他方法的对比，证明了该方法有着更高的准确率，更快的诊断速度，更好的鲁棒性。
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| 关 键 词： | 轴承故障诊断 卷积神经网络 双向门控循环单元 支持向量机 |
| 收稿时间： | 2021-05-06 |
Research on Bearing Fault Diagnosis of Improved One-dimensional Convolutional Neural Network and Bidirectional Gated Recurrent Unit |
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| Affiliation: | School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330033, China |
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| Abstract: | Aiming at the shortcomings of traditional intelligent fault diagnosis method that relies on manual experience for feature extraction and traditional convolutional neural networks (Convolutional neural networks, CNN) with too many parameters, too much training, and inability to make full use of time series information, a new deep learning algorithm based on an improved one-dimensional convolutional neural network and two-way gated loop unit is proposed. Firstly, the method uses the self-extraction ability of one-dimensional convolutional neural networks for feature extraction, and at the same time, a global mean pooling layer is designed to replace the fully connected layer of traditional convolutional neural networks to reduce the number of parameters. Secondly, the two-way gated loop unit is introduced to learn the time series relationship in the characteristic signal. Finally, the support vector machine is used to replace the traditional Softmax layer in CNN for fault classification, which further improves the accuracy of diagnosis. Experiments show that this method improves the accuracy of diagnosis to 99.8% and speeds up the diagnosis. Through comparison with other methods, it is proved that this method has higher accuracy, faster diagnosis speed and better robustness. |
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