# Technical Condition Assessment of Light-Alloy Wheel Rims Based on Acoustic Parameter Analysis Using a Neural Network

## TL;DR

This paper introduces a method to assess the condition of light-alloy wheel rims using acoustic parameters and a neural network, improving diagnostic accuracy in real-world settings.

## Contribution

A novel diagnostic method using acoustic features and a neural network for assessing wheel rim technical condition is proposed.

## Key findings

- The method effectively classifies rims as serviceable or unserviceable, including borderline cases.
- Acoustic parameters like T60, α, and E were successfully used for condition assessment.
- The approach shows potential for real-time monitoring and workshop diagnostics.

## Abstract

Light alloy wheel rims, despite their widespread use, remain vulnerable to fatigue-related defects and mechanical damage. This study presents a method for assessing their technical condition based on acoustic parameter analysis and classification using a deep neural network. Diagnostic data were collected using a custom-developed ADF (Acoustic Diagnostic Features) system, incorporating the reverberation time (T60), sound absorption coefficient (α), and acoustic energy (E). These parameters were measured during laboratory fatigue testing on a Wheel Resistance Test Rig (WRTR) and from used rims obtained under real-world operating conditions. The neural network was trained on WRTR data and subsequently employed to classify field samples as either “serviceable” or “unserviceable”. Results confirmed the high effectiveness of the proposed method, including its robustness in detecting borderline cases, as demonstrated in a case study involving a mechanically damaged rim. The developed approach offers potential support for diagnostic decision-making in workshop settings and may, in the future, serve as a foundation for sensor-based real-time rim condition monitoring.