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YOLO-MVCP: A Lightweight Fault Diagnosis Method of Rolling Bearing Based on STFT Time–frequency Graph and MobileViT Network Pruning

Author

Listed:
  • Yu Wang

    (National University of Defense Technology, National Key Laboratory of Equipment State Sensing and Smart Support
    National University of Defense Technology, College of Intelligence Science and Technology)

  • Yue Li

    (National University of Defense Technology, National Key Laboratory of Equipment State Sensing and Smart Support
    National University of Defense Technology, College of Intelligence Science and Technology)

  • Shufeng Zhang

    (National University of Defense Technology, National Key Laboratory of Equipment State Sensing and Smart Support
    National University of Defense Technology, College of Intelligence Science and Technology)

Abstract

To address the limitation that existing rolling bearing fault diagnosis methods are unable to simultaneously achieve high diagnostic accuracy and lightweight model deployment, a compact fault diagnosis approach is proposed based on short-time Fourier transform time–frequency representations and MobileViT network pruning. The initial step involves processing the raw vibration signal with a short-time Fourier transform to generate a 2D time–frequency image. Subsequently, a YOLO-MobileViT network is constructed to train the diagnostic model, and redundant secondary channels are eliminated through network pruning, enabling lightweight compression of the high-precision model following fine-tuning. Experimental validation on the CWRU dataset demonstrates that the YOLO-MVCP achieves a fault diagnosis accuracy of 100 percent, while maintaining a model size of only 1.24 MB. This balance between diagnostic accuracy and model compactness confirms its practical applicability and engineering value.

Suggested Citation

  • Yu Wang & Yue Li & Shufeng Zhang, 2026. "YOLO-MVCP: A Lightweight Fault Diagnosis Method of Rolling Bearing Based on STFT Time–frequency Graph and MobileViT Network Pruning," Springer Series in Reliability Engineering,, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-22873-4_24
    DOI: 10.1007/978-3-032-22873-4_24
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