机电工程技术2024，Vol.53Issue(11)：233-239,7.DOI:10.3969/j.issn.1009-9492.2024.11.050
基于离散S变换和改进深度残差网络的轴承故障诊断
Bearing Fault Diagnosis Based on Discrete S-transform and Improved Deep Residual Network
白金光 1张大明 2孙洋 3唐鑫 3陈忠3
作者信息
- 1. 深圳地铁建设集团有限公司,广东 深圳 518033
- 2. 日立电梯(广州)自动扶梯有限公司,广州 510660
- 3. 华南理工大学机械与汽车工程学院,广州 510640
- 折叠
摘要
Abstract
To improve the accuracy of rolling bearing fault diagnosis,a bearing fault diagnosis method based on discrete S-transform and improved deep residual network is proposed.firstly,two-dimensional feature extraction is performed on the collected rolling bearing data.Specifically,the data is processed using time-domain reconstruction(TDR),continuous wavelet transform(CWT),and discrete S(Stockwell)transform to create image datasets.This approach aims to extract temporal and spectral features that are inherent in the vibration data.Secondly,multiple neural networks such as ResNet residual network,VGG network,and AlexNet network are employed to train the image datasets using mini-batch gradient descent algorithm.Finally,various networks with various preprocessing methods are trained and evaluated on publicly available datasets with variable loads and speeds.The accuracy and standard deviation metrics are calculated on the test set to assess the performance of the models.The results demonstrate that the bearing fault diagnosis method based on discrete S-transform and improved deep residual network achieves higher recognition accuracy and better robustness,outperforming other training methods.This validates the effectiveness of the proposed method in rolling bearing fault diagnosis.关键词
轴承诊断/深度卷积神经网络/离散Stockwell变换/深度残差网络Key words
bearing diagnosis/deep convolutional neural network/discrete Stockwell transform/deep residual network分类
机械制造引用本文复制引用
白金光,张大明,孙洋,唐鑫,陈忠..基于离散S变换和改进深度残差网络的轴承故障诊断[J].机电工程技术,2024,53(11):233-239,7.
