Wheel Fault Detection Using Corrective Steering Angle Analysis
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Solution Overview
Problem
Existing methods cannot detect wheel defects such as misalignment or tire deflation in motor vehicles during driving situations, which can impact vehicle stability, safety, and the performance of advanced driver assistance systems, and do not specify which wheel is affected.
Innovation Solution
A method that automatically determines corrective steering wheel angles to keep the vehicle on a lane, calculates averages and standard deviations of these angles to detect faults, estimates the type of defect by analyzing time derivatives, and generates an alert message identifying the affected wheel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wheel alignment defects or tire deflation are detected only during maintenance visits using bench equipment, then measurement precision is improved, but loss of time increases and reliability decreases
Solution Approach 1:
The system performs preliminary detection of wheel defects during normal driving operations before maintenance is needed. The lateral control assistance system continuously monitors corrective steering angles and detects deviations that indicate wheel alignment defects or tire deflation, enabling early warning without requiring the vehicle to be on a maintenance bench.
Solution Approach 2:
The system enables the vehicle to self-diagnose wheel defects using its own lateral control assistance system during normal operation. The system uses the vehicle's existing sensors and control mechanisms to detect and identify wheel-related faults, eliminating the need for external bench equipment and maintenance visits for detection.
2Device complexity
If general fault detection is performed without specifying the affected wheel, then device complexity is reduced, but loss of information increases
Solution Approach 1:
The system segments the fault detection process into distinct analytical steps: first detecting general lateral control deviations, then analyzing the pattern of corrective steering angles to identify which specific wheel is affected, and finally categorizing the type of defect. This segmentation enables precise wheel identification without requiring complex additional hardware.
Solution Approach 2:
The system adds a temporal dimension to the analysis by examining the evolution of corrective steering angles over time during lateral control operations. By analyzing the time-series data of steering corrections and their relationship to detected lane markings, the system can infer which wheel is defective and what type of defect exists, transforming a static detection problem into a dynamic analysis.
Data Source
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AI summary
The subject matter of the invention is a method for detecting faults related to wheels of a motor vehicle in a driving situation, comprising: - a step (S1) of automatically determining a first series of corrective steering wheel angles applied successively to force the vehicle to follow a path parallel to a first rectilinear portion of a traffic lane; - a step (S2) of automatically detecting, from the corrective steering wheel angles of the first series, the presence of a fault affecting a pair of steering wheels of the vehicle; and, optionally: - a step (S3) of estimating a type of fault associated with the detected fault, a step (S4) of identifying the steering wheel of the pair of wheels affected by the fault; and - a step (S5) of generating a warning message for the attention of the driver of the motor vehicle, the message including the estimated type of fault and the wheel identified as having the fault.