High-Speed Bearing Dynamics and Applications in Production Lines

This study presents a novel approach to simulating and monitoring the dynamic performance of high-speed bearings, a critical component in automated industrial production for system efficiency and safety. The newly developed theoretical framework allows for detailed analysis of dynamic responses in t...

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Bibliographic Details
Published inInternational journal of simulation modelling Vol. 22; no. 4; pp. 701 - 711
Main Authors Zhang, L. J., Yang, S. J., Wang, S. J., Zeng, Y. M., Hua, W. C., Li, G. L.
Format Journal Article
LanguageEnglish
Published Vienna DAAAM International Vienna 01.12.2023
Subjects
Artificial neural networks
Condition monitoring
Critical components
Fault diagnosis
Fourier transforms
High speed
Performance assessment
Production lines
Real time
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Summary:This study presents a novel approach to simulating and monitoring the dynamic performance of high-speed bearings, a critical component in automated industrial production for system efficiency and safety. The newly developed theoretical framework allows for detailed analysis of dynamic responses in these bearings, especially under high-speed conditions. The Short-Time Fourier Transform (STFT) is used to capture time-frequency domain characteristics, while real-time condition monitoring is achieved through a deep learning-based Convolutional Neural Network, enhanced by a multi-head attention mechanism. This method enables managing large datasets, real-time surveillance, and accurate prediction of bearing conditions. Ultimately, this approach provides an innovative perspective for fault diagnosis and performance assessment of high-speed bearings in complex production environments. 21 refs.
ISSN:1726-4529
1726-4529
DOI:10.2507/IJSIMM22-4-CO17