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Lubricating Grease Thickness Classification of Steel Wire Rope Surface Based on GEMR-MobileViT.

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This study introduces GEMR-MobileViT, an efficient AI model for automated steel wire rope lubrication quality control. It accurately measures grease thickness, enhancing service life and enabling real-time site monitoring.

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computer Vision

Background:

  • Optimal grease thickness is crucial for steel wire rope longevity.
  • Automated quality control faces challenges from variable lighting and motion blur.
  • Existing models often have high computational complexity and parameters.

Purpose of the Study:

  • To develop an improved lightweight model for automated grease thickness identification on steel wire ropes.
  • To address limitations of existing models in practical, real-world conditions.
  • To enhance recognition accuracy while reducing model parameters and computational load.

Main Methods:

  • Proposed an improved lightweight GEMR-MobileViT model.
  • Incorporated GhostConv and an efficient multi-scale attention (EMA) module.
  • Utilized transfer learning and a custom dataset for training.

Main Results:

  • Achieved 96.63% recognition accuracy across five grease thickness categories.
  • Model has 4.19M parameters and 1.31 GFLOPs computational complexity.
  • Outperformed mainstream classification models in accuracy and efficiency.

Conclusions:

  • GEMR-MobileViT offers a superior balance between recognition precision and model size.
  • The model is suitable for deployment in steel wire rope lubrication sites for real-time monitoring.
  • Presents a novel approach for automating steel wire rope maintenance and extending service life.