用户名 密 码 登 录 忘记密码 免费注册

网站会员中心 | 设为首页 | 加入收藏

 您好，欢迎光临中国轴承行业网         退出登陆

会员中心 | 设为首页 | 加入收藏

协会

统计

国外文献 > 1.轴承 > 7. 智能方法与计算 > 神经网络

关键词搜索：

标 题： A Capsule Network with Deep Convolution Generative Adversarial Network Model for Rolling Bearing Fault Diagnosis
基于深度卷积生成对抗网络的胶囊网络滚动轴承故障诊断模型
作 者： Min Zhang
Xiaoyu Han
Qi Cheng
Shuangze Han
Xinyang Dong
Yunpeng Cao
Weixing Feng
关键词： Capsule network  [胶囊网络]
Deep convolution generative adversarial network  [ 深度卷积生成对抗网络]
Fault diagnosis  [ 故障诊断]
Neural network  [ 神经网络]
出版年： 2022
文集名： 34th Chinese Control and Decision Conference (CCDC 2022)
第34届中国控制与决策会议（CCDC 2022）
会议名： 34th Chinese Control and Decision Conference (CCDC 2022)
第34届中国控制与决策会议（CCDC 2022）
届 次： 34th
摘 要： In this study, a new fault diagnosis method named Capsule Network with Deep Convolution Generative Adversarial Network (DCGAN-CapsNet) is proposed for rolling bearings to cope with the insufficiency of CWRU (Case Western Reserve University) bearing data samples and the difficulty of network model training. First, a DCGAN model with four convolutional layers is designed to expand one-dimensional sample data. Second, a CapsNet with two convolutional layers is constructed, and the deep features of bearing fault data are extracted through the convolutional layer. Finally, the dynamic routing algorithm in the capsule neural network is applied to dynamically assign the features to the fault capsule category. By setting the threshold size to determine the probability of multiple faults, so as to complete the task of multi-fault identification. The proposed method alleviates the impact of insufficient data and the fault diagnosis accuracy rate exceeds 99%. The data set expanded by DCGAN is compared with the existing overlapping sampling expansion method, The prediction result of this model is compared with those of existing models such as the convolutional neural network. The effectiveness and potential of the proposed method are verified by experiments.

本研究提出了一种名为胶囊网络结合深度卷积生成对抗网络（DCGAN-CapsNet）的新型故障诊断方法，用于滚动轴承故障检测，以应对CWRU（西储大学）轴承数据样本不足以及网络模型训练困难的问题。首先，设计了一个包含四个卷积层的DCGAN模型，用于扩展一维样本数据； 其次，构建了一个包含两个卷积层的CapsNet，通过卷积层提取轴承故障数据的深层特征； 最后，采用胶囊神经网络中的动态路由算法，将特征动态分配至相应的故障胶囊类别。通过设定阈值大小来判断多故障发生的概率，从而完成多故障识别任务。所提方法有效缓解了数据不足带来的影响，故障诊断准确率超过99%。同时，对通过DCGAN扩展的数据集与现有重叠采样扩展方法进行了对比，并将该模型的预测结果与卷积神经网络等现有模型进行比较。所提出方法的有效性和潜力通过实验得到了验证。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

VIP网员

联系我们 | 登录注册 | 会员管理 | 网员服务 | 服务信息 | 广告服务 | 高薪聘请 | 法律声明 | 关于我们

京公网安备 11010202009788    京ICP备2021015424号