当前位置: X-MOL 学术J. Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Bearing Fault Diagnosis Based on Multiscale Convolutional Neural Network Using Data Augmentation
Journal of Sensors ( IF 1.9 ) Pub Date : 2021-02-22 , DOI: 10.1155/2021/6699637
Seungmin Han 1 , Seokju Oh 1 , Jongpil Jeong 1
Affiliation  

Bearings are one of the most important parts of a rotating machine. Bearing failure can lead to mechanical failure, financial loss, and even personal injury. In recent years, various deep learning techniques have been used to diagnose bearing faults in rotating machines. However, deep learning technology has a data imbalance problem because it requires huge amounts of data. To solve this problem, we used data augmentation techniques. In addition, Convolutional Neural Network, one of the deep learning models, is a method capable of performing feature learning without prior knowledge. However, since conventional fault diagnosis based on CNN can only extract single-scale features, not only useful information may be lost but also domain shift problems may occur. In this paper, we proposed a Multiscale Convolutional Neural Network (MSCNN) to extract more powerful and differentiated features from raw signals. MSCNN can learn more powerful feature expression than conventional CNN through multiscale convolution operation and reduce the number of parameters and training time. The proposed model proved better results and validated the effectiveness of the model compared to 2D-CNN and 1D-CNN.

中文翻译:

基于数据扩展的多尺度卷积神经网络轴承故障诊断

轴承是旋转机械最重要的部件之一。轴承故障可能导致机械故障,经济损失甚至人身伤害。近年来,已使用各种深度学习技术来诊断旋转机械中的轴承故障。但是,深度学习技术存在数据不平衡的问题,因为它需要大量数据。为了解决这个问题,我们使用了数据扩充技术。此外,深度学习模型之一的卷积神经网络是一种无需先验知识即可执行特征学习的方法。但是,由于基于CNN的常规故障诊断只能提取单尺度特征,因此不仅可能会丢失有用的信息,而且还会发生域移位问题。在本文中,我们提出了一种多尺度卷积神经网络(MSCNN),以从原始信号中提取更强大和更独特的特征。通过多尺度卷积运算,MSCNN可以学习比常规CNN更强大的特征表达,并减少参数数量和训练时间。与2D-CNN和1D-CNN相比,该模型证明了更好的结果并验证了模型的有效性。
更新日期:2021-02-22
down
wechat
bug