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 deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions
一种基于信息融合的深度卷积神经网络方法，用于不同工况下的轴承故障诊断 |
| 作 者： |
Tang Tang
Tianhao Hu
Ming Chen
Ronglai Lin
Guorui Chen |
| 关键词： |
Information fusion [信息融合]
generalization [ 泛化]
intelligent fault diagnosis [ 智能故障诊断]
convolutional neural networks [ 卷积神经网络]
deep learning [ 深度学习] |
| 出版年： |
2021 |
| 期刊名： |
Proceedings of the Institution of Mechanical Engineers, Part C. Journal of mechanical engineering science
机械工程师学会会刊，C部分。机械工程科学杂志 |
| 年卷期： |
2021, vol.235, no.8 |
| 摘 要： |
In recent years, deep learning-based fault diagnosis methods have drawn lots of attention. However, for most cases, the success of machine learning-based models relies on the circumstance that training data and testing data are under the same working condition, which is too strict for real implementation cases. Combined with the features of robustness of deep convolutional neural network and vibration signal characteristics, information fusion technology is introduced in this study to enhance the feature representation capability as well as the transferability of diagnosis models. With the basis of multi-sensors and narrow-band decomposition techniques, a convolutional architecture named fusion unit is proposed to extract multi-scale features from different sensors. The proposed method is tested on two data sets and has achieved relatively higher generalization ability when compared with several existing works, which demonstrates the effectiveness of our proposed fusion unit for feature extraction on both source task and target task.
近年来，基于深度学习的故障诊断方法受到了广泛关注。然而，对于大多数情况而言，机器学习模型的成功依赖于训练数据与测试数据处于相同工作环境这一前提，这在实际应用中过于严格。为此，本研究结合深度卷积神经网络的鲁棒性以及振动信号的特征，引入信息融合技术，以增强诊断模型的特征表达能力和迁移能力。基于多传感器和窄带分解技术，提出了一种名为融合单元的卷积架构，用于从不同传感器中提取多尺度特征。该方法在两个数据集上进行了测试，与现有多种方法相比，表现出较高的泛化能力，充分验证了所提出的融合单元在源任务和目标任务上的特征提取有效性。 |
| 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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