Real-time Diagnosis Method of Bearing Fault Based on Improved 1D-CNN

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更新时间：2023-07-24

- Real-time Diagnosis Method of Bearing Fault Based on Improved 1D-CNN
- “在电机故障检测领域，研究者提出了一种基于改进1D-CNN的智能诊断模型，通过引入残差结构，显著提升了模型的诊断效率和准确率，为电机故障实时检测提供了高效解决方案。”
  
  ”
- Software Guide Vol. 22, Issue 7, Pages: 32-37(2023)
- 作者机构：
  
  上海理工大学机械工程学院，上海 200093
- 作者简介：
- 基金信息：
- DOI：10.11907/rjdk.231177
  CLC： TH17
- Published：30 July 2023，
  
  Received：27 February 2023，
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季利鹏,郝健,曹家宁等.基于改进1D-CNN的轴承故障实时诊断方法[J].软件导刊,2023,22(07):32-37.

JI Lipeng,HAO Jian,CAO Jianing,et al.Real-time Diagnosis Method of Bearing Fault Based on Improved 1D-CNN[J].Software Guide,2023,22(07):32-37.
季利鹏,郝健,曹家宁等.基于改进1D-CNN的轴承故障实时诊断方法[J].软件导刊,2023,22(07):32-37. DOI： 10.11907/rjdk.231177.

JI Lipeng,HAO Jian,CAO Jianing,et al.Real-time Diagnosis Method of Bearing Fault Based on Improved 1D-CNN[J].Software Guide,2023,22(07):32-37. DOI： 10.11907/rjdk.231177.

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