Wheel Misalignment Detection via Steering Angle Statistics
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Solution Overview
Problem
Existing vehicle systems fail to efficiently detect and mitigate wheel misalignment, leading to potential damage and increased maintenance due to the inability to accurately identify misaligned wheels during operation.
Innovation Solution
A computer-based system that calculates recursive standard deviations and mean offsets of steering component angles to identify wheel misalignment by determining when the recursive standard deviation is below a deviation threshold and the recursive mean offset is above an offset threshold, allowing for timely compensation and prevention of wear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wheel misalignment is not detected, then vehicle operation continues normally, but wheel and vehicle parts suffer damage and maintenance needs increase
Solution Approach 1:
The system performs preliminary detection of wheel misalignment by continuously monitoring steering wheel angle data and calculating recursive standard deviation and mean offset during normal vehicle operation. This allows misalignment to be identified before it causes damage to wheels and vehicle parts, enabling preventive maintenance.
2Measurement precision
If traditional misalignment detection methods are used, then detection capability is limited, but detection time and accuracy are insufficient
Solution Approach 1:
The system implements continuous feedback by monitoring steering wheel angle data in real-time during vehicle operation. The computer calculates recursive standard deviation and mean offset from this data stream, providing ongoing feedback about wheel alignment status without requiring the vehicle to be stationary or removed from service.
Solution Approach 2:
The patent replaces traditional mechanical alignment detection methods with an electronic sensing and computational system. The computer uses algorithms to analyze steering wheel angle data and automatically determine misalignment conditions, substituting mechanical measurement tools with electronic detection and digital processing.
3Measurement precision
If steering wheel angle data is analyzed continuously, then misalignment detection accuracy improves, but computational complexity increases
Solution Approach 1:
The system transforms the raw steering wheel angle data into meaningful alignment indicators by calculating two specific parameters: recursive standard deviation (to detect variations from normal steering patterns) and mean offset (to detect sustained directional deviations). These parameter transformations convert complex continuous data into simple diagnostic criteria that can be evaluated against predetermined thresholds.
Data Source
AI summary
A system includes a computer including a processor and a memory, the memory storing instructions executable by the computer to determine a recursive standard deviation and a recursive mean offset of a plurality of steering component angles and to identify a wheel misalignment fault upon determining that the recursive standard deviation is below a deviation threshold and the recursive mean offset is above an offset threshold.