Adaptive Tire Temperature Curves for Early Failure Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing tire failure prediction methods for trucks and buses lack accuracy in determining tire abnormalities, particularly due to variations in load and speed, leading to potential tire bursts.
Innovation Solution
A tire failure prediction system that sets a master curve based on vehicle speed and heat build-up temperature, updates this curve using machine learning, and determines tire condition by comparing measured values to the master curve, with a warning system for abnormal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed master curve is used for tire temperature monitoring, then the system is simple to operate, but the measurement precision of tire abnormality detection deteriorates due to variations in load and speed
Solution Approach 1:
The master curve is transformed from a fixed static reference to a dynamic adaptive reference that automatically adjusts based on actual tire temperature measurements. The system updates the master curve parameters (At and Bt) in real-time to match current operating conditions, resolving the contradiction between operational simplicity and detection accuracy.
Solution Approach 2:
The system implements feedback by continuously comparing actual tire temperature measurements against the master curve and using this information to update the master curve parameters. This closed-loop feedback mechanism enables the system to adapt to varying load and speed conditions while maintaining accurate abnormality detection.
2Measurement precision
If the master curve is updated continuously to improve accuracy, then the measurement precision improves, but the use of energy increases due to frequent calculations and updates
Solution Approach 1:
Instead of continuous updates, the system performs master curve updates at periodic intervals or based on trigger conditions (such as significant changes in operating conditions). This periodic action reduces computational load and energy consumption while maintaining adequate measurement precision through selective updates.
3Adaptability or versatility
If the master curve is updated frequently to adapt to changing conditions, then the adaptability improves, but the loss of time increases due to processing delays
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing master curve parameters for different operating conditions. When actual conditions match predefined scenarios, the system can quickly retrieve and apply appropriate parameters without performing full recalculations, thus reducing processing time while maintaining adaptability.
Data Source
AI summary
A tire failure prediction system includes a setting unit configured to set a predetermined master curve indicating a relationship between a speed of a vehicle on which a tire is mounted and a heat build-up temperature of the tire, a determination unit configured to determine a tire condition of the tire based on a difference between the master curve set by the setting unit and a measured value of the heat build-up temperature of the tire, and an update unit configured to update the master curve set by the setting unit. The update unit updates the master curve in a case where the determination unit determines that the tire is normal. In a case where the determination unit determines that the tire is abnormal, a warning unit outputs a warning.