Intelligent Tire Wear Detection Method Based on PVDF Time-Frequency Domain Analysis

<div class="section abstract"><div class="htmlview paragraph">To obtain real-time tire wear status during vehicle operation, this paper proposes a tire wear detection method based on signal analysis. Firstly, PVDF piezoelectric thin film sensors are pasted in the center of the airtight layer of tires with different degrees of wear to collect tire stress data under different working conditions. Secondly, filter and extract the time-domain and frequency-domain feature information of the collected data to construct a feature dataset. Finally, a deep regression model is established to train the feature dataset and achieve real-time detection of tire damage status. The results indicate that the prediction algorithm based on signal analysis and feature extraction achieves a maximum error of 0.3mm in tire wear detection, demonstrating high accuracy in tire wear detection. Providing tire information for safe driving of vehicles has high industrial application value.</div></div>

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Publication Details

Journal
SAE technical papers on CD-ROM/SAE technical paper series
Published
2025-04-01
DOI
https://doi.org/10.4271/2025-01-8278
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Intelligent Tire Wear Detection Method Based on PVDF Time-Frequency Domain Analysis

Lisheng Jin, Yang Hao, Xie Xianyi
SAE technical papers on CD-ROM/SAE technical paper series
Vehicle Dynamics and Control Systems
article

Intelligent Tire Wear Detection Method Based on PVDF Time-Frequency Domain Analysis

Lisheng Jin, Yang Hao, Xie Xianyi
article en

Abstract

<div class="section abstract"><div class="htmlview paragraph">To obtain real-time tire wear status during vehicle operation, this paper proposes a tire wear detection method based on signal analysis. Firstly, PVDF piezoelectric thin film sensors are pasted in the center of the airtight layer of tires with different degrees of wear to collect tire stress data under different working conditions. Secondly, filter and extract the time-domain and frequency-domain feature information of the collected data to construct a feature dataset. Finally, a deep regression model is established to train the feature dataset and achieve real-time detection of tire damage status. The results indicate that the prediction algorithm based on signal analysis and feature extraction achieves a maximum error of 0.3mm in tire wear detection, demonstrating high accuracy in tire wear detection. Providing tire information for safe driving of vehicles has high industrial application value.</div></div>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
Yanshan University (CN)
Openalex Percentile: Top 94%
Vehicle Dynamics and Control Systems
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Intelligent Tire Wear Detection Method Based on PVDF Time-Frequency Domain Analysis — Lisheng Jin, Yang Hao, et al. · SAE technical papers on CD-ROM/SAE technical paper series (2025) | TGRS Research Map | TGRS