Presentation Details
Fault Identification through harmonic dynamics of Bearing-Rotating systems
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Updated Time：2025-11-10 10:13:40 Hits：107
Oral Presentation
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Abstract
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. The operational status of rotating machinery directly affects factory production efficiency, safety, and costs. Most failures in rotating machinery are caused by faults in key components, among which bearings are the most frequently faulty parts. Therefore, research on bearing fault diagnosis technology has significant theoretical and practical value.
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.
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.
Keywords
bearing fault diagnosis;,Neural network,harmonic content
Speaker
Chengfei Li
Dr. Zhejiang Academy of Special Equipment ScienceSubmission Author
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Important Date
-
Conference Date
Nov 21
2025
toNov 23
2025
-
Oct 20 2025
Draft paper submission deadline
-
Dec 08 2025
Registration deadline
Sponsored By
IEEE Instrumentation and Measurement Society
South China University of Technology
South China University of Technology
Organized By
South China University of Technology
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