Tire Pressure Monitoring Auto Learn Algorithm
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Solution Overview
Problem
Existing tire pressure monitoring systems require manual intervention to associate new sensors with a receiver, leading to potential incorrect associations and customer dissatisfaction, as well as the risk of incorrectly identifying sensors from nearby vehicles.
Innovation Solution
An auto learn algorithm that tracks burst transmissions from sensors, builds a Pareto of potential sensor identifications based on the greatest number of received transmissions, filters these identifications, and assigns them to respective tire locations, eliminating the need for manual technician intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used to associate new sensors with the receiver, then the association process is simple and direct, but it requires service technician involvement and increases the potential for human error
Solution Approach 1:
The system automatically performs sensor association without requiring service technician intervention. The receiver autonomously monitors burst transmissions, builds Pareto lists of sensor IDs, and completes the association process independently, eliminating manual operations while maintaining high accuracy through algorithmic validation
Solution Approach 2:
The manual mechanical process of technician intervention is replaced with an electronic/algorithmic system. The receiver uses automated signal processing and Pareto-based algorithms to perform sensor identification and association, substituting human操作 with electronic automation
2Ease of operation
If auto learn functions are implemented to automatically associate sensors, then service technician intervention is eliminated, but there is risk of incorrectly associating sensors from nearby vehicles
Solution Approach 1:
The system applies different association criteria based on local conditions at each tire position. By monitoring which sensor IDs appear in burst transmissions during vehicle operation, the system identifies sensors specifically associated with each wheel location, ensuring that only locally relevant sensors are associated with corresponding tire positions
Solution Approach 2:
The association process is dynamic rather than static. The receiver continuously monitors burst transmissions during vehicle operation and updates the Pareto list of sensor IDs in real-time, allowing the system to adapt to actual sensor presence and correctly identify sensors based on their operational patterns rather than pre-programmed associations
3Reliability
If manual association is performed, then incorrect associations are minimized, but the process requires customer or technician involvement and increases service time
Solution Approach 1:
The system performs preliminary monitoring of burst transmissions during normal vehicle operation to automatically build the Pareto list of sensor IDs before final association is completed. This preliminary action occurs during routine driving, eliminating the need for separate service appointments while ensuring accurate sensor identification through accumulated transmission data
Solution Approach 2:
The receiver continuously monitors burst transmissions during vehicle operation rather than requiring discrete manual intervention events. This continuous monitoring allows the system to accumulate sensor identification data naturally during normal use, eliminating service time losses while maintaining association accuracy through ongoing validation
Data Source
AI summary
A method of operating a tire pressure monitoring system on a vehicle comprising tracking number of burst transmissions sent by a sensor, building a pareto of potential sensor identifications based on the greatest number of burst transmissions received from the sensor associating a potential sensor identification to a respective tire location on the vehicle and storing the associated sensor identification in memory. A tire pressure monitoring system comprising a plurality of tires in respective locations, each of the plurality of tires having a sensor, at least one sensor capable of burst mode transmission, and an auto learn function in a controller coupled to the sensors in the plurality of tires, the controller receiving and counting burst transmissions from the sensor for a predetermined time, the controller creating a pareto of received sensor identifications, filtering the pareto of potential sensor identifications and assigning the potential sensor identifications to a respective tire in the plurality of tires.