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Can tire wear be detected using vibration signals?–an experimental study

Author

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  • C. V. Prasshanth

    (Vellore Institute of Technology)

  • V. Sugumaran

    (Vellore Institute of Technology)

Abstract

In modern transportation, tire wear monitoring is critical for road safety, fuel efficiency, and overall vehicle performance. Tire wear compromises traction, leading to hazardous conditions and increased maintenance costs. This study explores tire wear monitoring across five conditions—25, 50, 75, 100%, and a good-condition tire—using vibration signals captured via an accelerometer. Advanced feature extraction techniques, including statistical, histogram, and autoregressive moving average (ARMA) features, were employed, followed by feature selection using the J48 decision tree algorithm. A comparative analysis of 13 tree-based classifiers demonstrated that Random Forest paired with ARMA features achieved a classification accuracy of 100% for training, cross-validation, and testing datasets, with computation times of 0 s for the training and testing sets, and 0.03 s for cross-validation. This robust system showcases high reliability and adaptability, significantly advancing real-world diagnostics. These findings emphasize the proactive role of automated tire wear monitoring in enhancing road safety and environmental sustainability.

Suggested Citation

  • C. V. Prasshanth & V. Sugumaran, 2025. "Can tire wear be detected using vibration signals?–an experimental study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(5), pages 1899-1913, May.
    • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:5:d:10.1007_s13198-025-02756-x
    DOI: 10.1007/s13198-025-02756-x
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