Peer-Based Tire Health Monitoring via Self and Peer Comparison
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Solution Overview
Problem
Conventional tire pressure monitoring systems face challenges in accurately and timely detecting tire anomalies, particularly in commercial vehicles with multiple tires, due to factors like heat emission, thermal dissipation, and load variations, leading to potential tire failures and increased downtime.
Innovation Solution
A method and apparatus that utilize individual self-comparison and peer-based comparison to normalize tire data, detect anomalies such as severe leakage and inflation, and predict slow leaks based on historical trends, allowing for dynamic threshold adjustments and retraining of tire-specific models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tire pressure monitoring systems use simple threshold alerts, then the system complexity is low, but the detection accuracy and timeliness deteriorate due to false alarms and late detection of slow leaks
Solution Approach 1:
The system performs preliminary normalization of tire data using self-comparison (comparing each tire's data to its own historical baseline) and peer-based comparison (comparing to other tires on the same vehicle) before anomaly detection. This preliminary processing establishes expected ranges and patterns, enabling more accurate detection without requiring overly complex real-time analysis.
Solution Approach 2:
The patent introduces intermediate processing layers including data normalization, baseline establishment, and comparative analysis between self and peers. These intermediary steps transform raw sensor data into normalized, comparable metrics that improve detection accuracy while keeping the overall system architecture manageable through modular processing stages.
2Loss of time
If the system monitors all tires continuously with detailed analysis, then the detection timeliness improves, but the energy consumption and computational load increase
Solution Approach 1:
The system applies partial monitoring intensity by focusing detailed analysis only on tires that show deviations from their baselines or peer averages. Normal tires receive minimal processing, while potentially problematic tires receive intensified scrutiny. This selective approach reduces overall computational load and energy consumption while maintaining timely detection capability for anomalies.
3Adaptability or versatility
If the system uses fixed threshold alerts, then the ease of operation is high, but the adaptability to different loading conditions and environmental factors deteriorates
Solution Approach 1:
The system dynamically adjusts monitoring thresholds and baselines based on actual operating conditions. Each tire develops its own dynamic baseline from historical data, and thresholds adapt to account for environmental factors and loading patterns. This dynamic adaptation improves versatility across different conditions while the system maintains automated operation that preserves ease of use.
Solution Approach 2:
The patent changes the parameter reference points from fixed thresholds to dynamic baselines derived from historical data and peer comparisons. Monitoring parameters such as pressure and temperature are evaluated relative to adaptive thresholds that change with loading conditions, temperature, and usage patterns, enabling the system to adapt to varying operational environments.
Data Source
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AI summary
A method of monitoring vehicle tires is disclosed. The method comprising: receiving tire data associated with a plurality of tires on a vehicle; and determining a health condition for each of the plurality of tires by performing an individual self-comparison and a peer-based comparison of the tire data for each tire, wherein the individual self-comparison reduces variances between the plurality of tires on the vehicle, and wherein the peer-based comparison reduces variances within each tire. Furthermore, an apparatus is disclosed comprising: at least one processor; and program code configured upon execution by the at least one processor to monitor vehicle tires by receiving tire data associated with a plurality of tires on a vehicle, and determining a health condition for each of the plurality of tires by performing an individual self-comparison and a peer-based comparison of the tire data for each tire.