Tire Wear Estimation Using Noise-Factor Radius Grouping
Solution Overview
Problem
Existing tire wear estimation methods are inaccurate due to variations in noise factors such as tire pressure, load, and speed, leading to deviations between estimated and actual remaining useful life.
Innovation Solution
A system that detects noise factors like tire pressure, load, and speed, groups radii based on discrete sets of these factors, and estimates a transition instance of tire health condition using probabilistic or deterministic models to reduce estimation deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tire wear estimation methods are used, then the estimation process is simple, but the accuracy of remaining useful life prediction deteriorates due to noise factor variations
Solution Approach 1:
The patent segments the tire wear estimation process by dividing radii measurements into multiple groups based on discrete sets of noise factors (pressure, load, speed). Each group corresponds to specific operating conditions, allowing separate analysis and extrapolation for each segment. This segmentation enables accurate prediction of transition instances for different operating scenarios while maintaining manageable system complexity through structured data organization.
Solution Approach 2:
The patent applies parameter changes by transforming continuous noise factor values (pressure, load, speed) into discrete sets or categories. This discretization allows the system to handle variations in operating conditions by creating distinct groups for different parameter ranges, thereby improving estimation accuracy without requiring complex continuous variable modeling.
2Reliability
If noise factors are not considered, then the estimation system is simpler, but the deviation between estimated and actual remaining useful life increases
Solution Approach 1:
The patent transforms continuous noise factor parameters into discrete sets, creating categorical groupings for pressure, load, and speed. This parameter transformation enables the system to account for operating condition variations by assigning measurements to discrete groups, thereby improving prediction reliability while keeping data processing complexity manageable through structured categorization.
Solution Approach 2:
The patent segments radii measurements into multiple groups based on discrete noise factor sets. Each group represents specific operating conditions, allowing the system to analyze wear patterns separately for different scenarios. This segmentation improves reliability by ensuring that predictions account for the specific conditions under which the tire operates, while maintaining organized and manageable data structures.
3Measurement precision
If discrete sets of noise factors are used for grouping, then the estimation accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent applies parameter changes by converting continuous noise factor values into discrete sets or categorical groups. This transformation enables precise grouping of radii measurements based on specific operating conditions while maintaining manageable data processing complexity through structured categorization. The discrete parameter representation simplifies the grouping operation compared to handling continuous variables.
Solution Approach 2:
The patent segments the data into multiple groups based on discrete noise factor sets, with each group representing specific operating conditions. This segmentation improves transition instance estimation precision by allowing separate extrapolation for each condition group, while the structured grouping approach keeps the overall system complexity manageable through organized data management.
Data Source
AI summary
For respective instances, noise factors of a vehicle tire are detected. The noise factors include a tire pressure, a load, and a tire speed. For the respective instances, respective radii of the vehicle tire are determined. The respective radii are grouped based on respective discrete sets of noise factors. A transition instance of a health condition for the vehicle tire is estimated based on extrapolating the respective grouped radii.