国外文献 > 1.轴承 > 7. 智能方法与计算 > 机器学习
关键词搜索：
| 标 题： |
A Batch-normalized Deep Neural Networks and its application in bearing fault diagnosis
批归一化深度神经网络及其在轴承故障诊断中的应用 |
| 作 者： |
Jiaqi Zheng
Hongjun Sun
Xiaojing Wang
Jun Liu
Caizhi Zhu |
| 关键词： |
Deep learning [深度学习]
Fault diagnosis [ 故障诊断]
Auto-encoder [ 自编码器]
Batch normalization [ 批量归一化] |
| 出版年： |
2019 |
| 会议名： |
11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC 2019)
第11届智能人机系统与控制论国际会议（IHMSC 2019） |
| 届 次： |
11th |
| 摘 要： |
At present, in the field of fault diagnosis, deep learning has shown state-of-the-art performance in processing mechanical big data. This paper studies the deep neural networks (DNN) model based on auto-encoder, which has high performance in bearing fault diagnosis. However, the traditional structure of stacked auto-encoders has the problem of internal covariant transfer, that inhibits the training efficiency and generalization ability of the network. To overcome the aforementioned deficiency and further explore the performance of DNN, a batch normalization layer is employed in the fully connected layer of the DNN during training, so the network can obtain the stable distribution of activation values. Therefore, this paper proposes a new intelligent diagnosis method named batch normalization deep neural networks (BN-DNN). Finally, the experimental results show that: (1) The performance of BN-DNN is better than DNN. (2) BN-DNN can directly process the raw vibration signals, and the diagnostic accuracy can be maintained above 99% under different working conditions.
目前，在故障诊断领域，深度学习在处理机械大数据方面已展现出前沿水平。本文研究了一种基于自编码器的深度神经网络（DNN）模型，该模型在轴承故障诊断中具有优异性能。然而，传统的堆叠自编码器结构存在内部共变传递问题，这会限制网络的训练效率和泛化能力。为克服上述缺陷并进一步提升DNN性能，在训练过程中于DNN全连接层引入了批归一化层，使网络能够获得稳定的激活值分布。因此，本文提出一种名为批归一化深度神经网络（BN-DNN）的新智能诊断方法。实验结果表明：（1）BN-DNN的性能优于DNN； （2）BN-DNN可直接处理原始振动信号，在不同工作条件下仍能保持诊断准确率超过99%。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
京公网安备 11010202009788