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Fault Diagnosis of Bearing–Rotor Systems via Harmonic Dynamic Responses
编号：109 访问权限：仅限参会人 更新：2026-09-29 21:50:53 浏览：8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]

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摘要
Rotating machinery plays a crucial role in industrial production and is one of the most widely used types of equipment, including generators, motors, steam turbines, pumps, etc. This paper takes bearings as the research object and designs a bearing fault diagnosis algorithm by extracting the harmonic dynamic characteristics of bearing vibration signals and combining them with a Convolutional Neural Network (CNN) model. The algorithm analyzes the spectral distribution of faulty bearing vibration signals based on Fourier Transform, utilizes signal fitting algorithms to extract the harmonic dynamic characteristics of the vibration signals, then inputs the feature data into the CNN for learning and testing. Finally, it is validated on the CWRU dataset, achieving a diagnosis success rate as high as 90.62% on a multi-condition ten-classification problem. The research aims to achieve better bearing diagnosis results, intending to provide new ideas and methods for the development of bearing fault diagnosis technology and offer more reliable assurance for the stable operation of rotating machinery in industrial production.
关键词
Bearing fault diagnosis, Harmonic dynamic characteristics, Convolutional Neural Network
报告人
Chengfei Li
Dr. Zhejiang Academy of Special Equipment Science；Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang

稿件作者
Chengfei Li Zhejiang Academy of Special Equipment Science；Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang
Jianfeng Jiang Zhejiang Academy of Special Equipment Science
Hao Wang Zhejiang Academy of Special Equipment Science
Rui Sun Zhejiang Academy of Special Equipment Science
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重要日期

  • 会议日期

    11月06日

    2026

    至

    11月08日

    2026

  • 10月15日 2026

    初稿截稿日期

主办单位
IEEE Instrumentation and Measurement Society
承办单位
Sichuan University
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