Axle Cross-Comparison TPMS for Slow Tire Leak Detection
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Solution Overview
Problem
Existing TPMS systems lack precision in detecting slow tire pressure leaks, particularly under varying driving conditions and temperatures, and fail to distinguish between rapid and slow leaks, leading to unnecessary alerts and maintenance actions.
Innovation Solution
A tire pressure monitoring system that uses sensors on opposite tires of an axle to measure and compare pressure differences, employing a noise filter and Kalman filter to refine data, and a detection module to identify leaks based on pressure trends and deviations from predetermined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tire pressure is monitored using conventional TPMS systems that compare pressure to a threshold value, then the system can detect when pressure drops below the threshold, but the system lacks precision in detecting slow leaks and generates unnecessary alerts due to inability to distinguish between rapid and slow pressure changes
Solution Approach 1:
The system dynamically adapts the threshold for leak detection based on the rate of pressure change. By monitoring the temporal derivative of pressure (dp/dt), the system adjusts its sensitivity - using higher thresholds for rapid changes (likely normal operation) and lower thresholds for slow changes (potential leaks). This dynamic thresholding resolves the contradiction by making the detection precision adaptive to the specific condition being monitored.
Solution Approach 2:
The system changes the detection parameter from a static pressure threshold to a dynamic threshold that incorporates the rate of pressure change. By introducing the time-derivative parameter (dp/dt) into the detection logic, the system can distinguish between rapid pressure changes (which may be normal) and slow pressure changes (which indicate leaks), thereby improving both detection precision and alert accuracy simultaneously.
2Device complexity
If the system monitors only tire pressure without additional sensors, then the system complexity is reduced, but the system cannot account for driving conditions and temperature conditions affecting pressure measurements
Solution Approach 1:
The system introduces an intermediary computational model (the tire pressure model) that simulates expected pressure behavior based on tire characteristics, temperature, and driving conditions. This virtual sensor acts as a mediator between the simple pressure measurement and the complex environmental factors, allowing the system to achieve high measurement precision without adding physical sensors. The model compensates for temperature effects and driving condition variations purely through computation.
Solution Approach 2:
The system replaces physical sensors (temperature sensors, acceleration sensors) with a computational model that mathematically accounts for these factors. By substituting mechanical sensing with algorithmic compensation, the system maintains measurement precision while minimizing device complexity. The tire pressure model uses mathematical relationships to predict how temperature and driving dynamics affect pressure, eliminating the need for additional hardware sensors.
3Reliability
If the system generates alerts for any pressure drop below threshold, then all potential issues are captured, but fleet operations are disrupted by unnecessary vehicle removals from service
Solution Approach 1:
The system applies partial detection action by not alerting on all pressure drops, but only on those that meet specific criteria (slow rate of change, sustained below threshold). By being selective rather than comprehensive, the system avoids unnecessary alerts while still capturing genuine leak conditions. This partial action approach maintains sufficient detection coverage for actual problems while eliminating false positives that would disrupt fleet operations.
Solution Approach 2:
The system performs preliminary analysis of the pressure change rate before generating an alert. By evaluating dp/dt in advance, the system pre-filters out rapid pressure changes that are likely normal operation. This preliminary action prevents unnecessary alerts before they reach the operator, maintaining detection coverage for genuine leaks while preserving vehicle availability for normal operations.
Data Source
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AI summary
A tire pressure monitoring system (10) and method is disclosed. The system comprises: a first tire (12a, 12b, 12c, 12d) mounted on a first end of an axle (88, 90) and a second tire (12a, 12b, 12c, 12d) mounted on a second end of the axle (88, 90), wherein the first and second tires and the axle support a vehicle (14); a first sensor (30) mounted on the first tire for measuring a pressure of the first tire; a second sensor (30) mounted on the second tire for measuring a pressure of the second tire;means (36) for transmitting measured pressure data from the first sensor and the second sensor to a processor (38); and a tire pressure model (54) configured for being executed on the processor (38), the tire pressure model (54) including: an aggregator (58) that accumulates the measured pressure data (60) from the first sensor and the second sensor; a noise filter (70) that is configured to filter sensor noise and to generate filtered pressure data (72) from the measured tire pressure data (60); a detection module (76) configured for receiving the filtered pressure data (72) and for determining when an air pressure leak occurs in at least one of the first tire and the second tire; and a leak notification (104) which can be generated by the tire pressure model (54) when the air pressure leak occurs.