Tire Performance Estimation Using Stored Coefficients After Data Gaps
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Solution Overview
Problem
Existing tire monitoring systems face challenges in determining tire performance characteristics due to gaps in tire parameter data, especially when new monitoring devices are installed on existing wheels, leading to insufficient data for accurate analysis.
Innovation Solution
A method involving a processing system that queries memory for tire parameter values, determines insufficiency, and retrieves a performance coefficient to determine tire performance characteristics, even in data gaps, by providing indications for required data and using stored coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If new tire monitoring devices are installed on existing wheels, then the monitoring system can track tire parameters, but gaps in tire parameter data occur leading to insufficient data for determining tire performance characteristics
Solution Approach 1:
The system performs preliminary actions by detecting the presence of a new tire monitoring device before data gaps fully develop. The processing system identifies when a new device is installed and proactively retrieves historical performance coefficients from memory, preventing the data insufficiency problem from occurring. This early detection and response mechanism ensures continuous tire performance monitoring despite device replacements or installations.
2Measurement precision
If tire parameter data is collected over extended periods to enable trend analysis, then accurate tire performance characteristics can be determined, but latency and resource requirements increase
Solution Approach 1:
Performance coefficients are pre-calculated and stored in memory during periods when sufficient tire parameter data is available. When data gaps occur or new monitoring devices are installed, the system immediately retrieves these pre-stored coefficients rather than waiting to collect new data. This preliminary preparation of performance data eliminates the need for extended data collection periods, reducing latency while maintaining measurement precision.
3Measurement precision
If tire parameter data spanning particular length of time and containing predetermined number of flight cycles is collected, then trend analysis can be performed, but device complexity and data storage requirements increase
Solution Approach 1:
The system extracts only the essential performance characteristics from extensive tire parameter data by calculating performance coefficients that encapsulate trend information. Instead of storing and processing all raw tire parameter data over multiple flight cycles, the system extracts key performance metrics and stores them as compact coefficients in memory. This extraction approach maintains trend analysis capability while significantly reducing data storage requirements and device complexity.
Data Source
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AI summary
Disclosed is a method of determining a tire performance characteristic of a tire including querying, using a processing system, a memory as to a presence of a first set of values indicative of a tire parameter of a tire in the memory, and determining, using the processing system, that the first set of values is insufficient to determine the tire performance characteristic of the tire, The method includes performing steps, using the processing system, comprising one or more of providing an indication that a second set of values indicative of the tire parameter is required to determine the tire performance characteristic of the tire, and retrieving, from the memory, a performance coefficient of the tire corresponding to performance of the tire associated with the first set of values, and determining, based on the performance coefficient, the tire performance characteristic of the tire.