Tire Replacement Forecasting Using Vehicle Data and Wear Models
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Solution Overview
Problem
Existing tire monitoring systems face challenges in accurately and reliably estimating tire wear rate and predicting tire replacement time due to issues with sensor mounting, data transmission, and accuracy in indirect measurement techniques.
Innovation Solution
A tire replacement system that includes a processor connected to the vehicle's electronic system, a prediction model using survival analysis and machine learning to estimate remaining tire life, and a notification system for timely replacement, enhancing accuracy and reliability through data from sensors and vehicle systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If direct sensor measurement techniques are used to measure tire wear state, then measurement simplicity is improved, but sensor mounting complexity and reliability deteriorate
Solution Approach 1:
The patent uses an intermediary approach by measuring parameters that are easier to obtain (vehicle speed, distance traveled, tire pressure, temperature) and using these as inputs to a prediction model that estimates wear rate. This mediator layer (the prediction model) translates easily measurable parameters into reliable wear state estimates without requiring direct sensors on the tire tread.
Solution Approach 2:
The patent replaces direct mechanical sensor measurement systems with an information processing system. Instead of using physical sensors to directly measure tread depth, the system uses electronic data from vehicle sensors combined with a prediction model (survival analysis and machine learning) to calculate wear rate, thereby eliminating the need for complex sensor mounting on the tire.
2Reliability
If indirect measurement techniques are used to estimate tire wear rate, then sensor mounting challenges are eliminated, but estimation accuracy deteriorates
Solution Approach 1:
The patent changes the parameters being measured from direct tread depth (difficult to measure) to multiple auxiliary parameters (vehicle speed, distance, tire pressure, temperature) that are easily and reliably obtained from vehicle systems. By changing what parameters are measured and how they are combined through prediction models, the system achieves both reliability and accuracy.
Solution Approach 2:
The patent creates a composite estimation approach by combining multiple data sources (vehicle speed, distance traveled, tire pressure, temperature) with multiple prediction techniques (survival analysis, machine learning models). This composite approach leverages the strengths of each input parameter and model to achieve high estimation accuracy without requiring any single direct measurement.
3Measurement precision
If multiple prediction models are used to estimate tire wear rate, then estimation accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent creates a universal prediction system that handles multiple tire types, usage conditions, and wear patterns through a single integrated framework. The system uses a family of prediction models (survival analysis and machine learning) that can be applied across different vehicle types and tire configurations, reducing the need for multiple specialized systems while maintaining high accuracy.
Data Source
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AI summary
A replacement system and method for a tire (12) supporting a vehicle (14), the vehicle including an electronic system (42), is disclosed. The system (10) comprises an electronic memory capacity for storing identification information for the tire (12); a processor (38) in electronic communication with the electronic system (42), the processor receiving identification information for the tire (12) from the electronic memory capacity and vehicle data from the electronic system (42) of the vehicle (14); a prediction model in electronic communication with the processor (38) and receiving the identification information for the tire (12) and the vehicle data; an identification means of a replacement tread depth for the tire (12) included in the prediction model; an estimation means of remaining available distance for the tire (12) to reach the replacement tread depth being determined by the prediction model; an estimation means of remaining available time to reach the replacement tread depth being determined by the prediction model; a residual correction module in electronic communication with the processor to optimize the estimation of the remaining available time for the tire to reach the replacement tread depth; a replacement lead time determination being generated by the tire replacement system; and a notification of the replacement lead time transmitted to at least one of the electronic system (42) of the vehicle (14), a cloud-based server (44), and a display device (50).