Tyre Abnormality Detection Using Acoustic and Wheel Speed Signals

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Solution Overview

Problem

Existing methods for detecting tire anomalies, such as low tire pressure, are inefficient due to irregular measurements, leading to complex and costly estimation of tire conditions.

Innovation Solution

A method involving assigning representative values to measured state variables and performing time series analysis to reliably and cost-effectively estimate the temporal development of tire conditions, using techniques like neural networks, decision trees, and autoregressive models.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If acoustic sensors are used to detect tyre abnormalities, then tyre safety can be monitored, but the sensors are susceptible to extraneous noise from road surface and environmental factors

Engineering Contradiction:
Improvetyre safety monitoring accuracyVSAvoidextraneous noise from road surface and environment
Core Design Contradiction:
ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The patent introduces wheel speed data as an intermediary reference signal to distinguish tyre anomaly acoustic emissions from extraneous noise. By comparing acoustic sensor signals against wheel speed data, the system can identify when noise correlates with wheel rotation (road surface noise) versus when it represents actual tyre anomalies, thereby filtering harmful environmental factors while maintaining reliable monitoring.

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The system integrates multiple data sources (acoustic sensors, wheel speed sensors, and anomaly detection logic) into a unified tyre monitoring platform. This multi-functional approach allows the same system to perform both noise filtering and anomaly detection, improving reliability without requiring separate dedicated systems for each function.

Inventive Principle:
Principle #6Universality (Multi-functionality)

2Measurement precision

If multiple data sources are integrated for comprehensive tyre monitoring, then detection accuracy improves, but system complexity increases

Engineering Contradiction:
Improvetyre anomaly detection accuracyVSAvoidsystem integration complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The patent divides the tyre monitoring system into distinct functional modules: acoustic sensor module, wheel speed sensor module, and anomaly detection module. Each module operates independently with clearly defined inputs and outputs, making the overall system easier to implement, test, and maintain while still achieving comprehensive monitoring through their coordinated interaction.

Inventive Principle:
Principle #1Segmentation

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables simpler and more accurate estimation of tire conditions by disregarding erroneous measurements and providing usable data for timely tire anomaly detection.

Implementation Method 1

acoustic sensor (122) configured to detect acoustic emissions from the tyre (102)

Methodology Applied
Scientific EffectAcoustic radiation pressure: Acoustic Radiation Pressure

Data Source

PatentEP4146487B1Method and system for detecting tyre abnormalities
Publication Date: 2026.05.06 CONTINENTAL REIFEN DEUTSCHLAND GMBH
  • EP4146487B1 patent drawingFigure 1

AI summary

The invention relates to a method for detecting tyre abnormalities, said method comprising the following steps: - providing a vehicle tyre (4); - providing a measuring device (2), the measuring device (2) being suitable for measuring a state variable of the vehicle tyre (2); - detecting time intervals in which measurements are carried out by means of the measuring device (2); - measuring a state variable during the time interval; - associating a representative value with the measured state variable; - carrying out a time series analysis of the representative values.