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A hybrid muti-dimension normalization layers improved ResNet based fault diagnosis method of rolling bearing
编号：58 访问权限：公开 更新：2022-12-21 16:52:44 浏览：342次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

所在会场：[暂无会议] [暂无会议段]

演示文件
摘要
CNN, a kind of deep learning method, has been widely used in fault diagnosis. It requires a large number of training samples, but it is difficult to obtain abundant samples under different conditions. Aiming at insufficient fault samples, an improved ResNet (IResNet) is proposed in this paper. Firstly, order spectrum is computed from raw data as pre-processed samples, which will be further augmented to improve the generalization ability of the model. Secondly, IResNet is constructed by several hybrid residual building blocks fused from multi-dimensional normalization layers, which can be adopted to enhance the feature extraction ability of the model. Then, the parameters of IResNet in the source domain are transferred to identify the health status of rolling bearing in the target domain. Finally, experimental data under different working conditions are used to verify the performance of the proposed method. The experimental results indicate that the recognition accuracy of the proposed method is higher than other methods and that the proposed method can identify the health status of rolling bearing with small training samples.
关键词
Convolutional neural network;Improved ResNet;Hybrid block;Rolling bearing;Fault diagnosis
报告人
长波 贺
讲师 安徽大学

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重要日期

  • 会议日期

    11月30日

    2022

    至

    12月02日

    2022

  • 11月30日 2022

    初稿截稿日期

  • 12月24日 2022

    报告提交截止日期

  • 04月13日 2023

    注册截止日期

主办单位
Harbin Insititute of Technology
China Instrument and Control Society
Heilongjiang Instrument and Control Society
Chinese Institute of Electronics
IEEE I&M Society Harbin Chapter
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