Adaptive Threshold Sensor Fault Detection in Vehicle Stability Systems
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Solution Overview
Problem
Current vehicle stability systems face challenges in quickly detecting and isolating faults in sensors and actuators to prevent improper system responses and ensure timely maintenance, particularly due to the lack of redundancy in certain sensors like the hand-wheel angle sensor.
Innovation Solution
The system employs adaptive thresholds and multi-model estimators to compare sensor measurements with model-based estimates, using physical and analytical redundancy to detect faults in yaw rate, lateral acceleration, and hand-wheel angle sensors, with adaptive thresholds adjusting based on vehicle operating conditions to enhance sensitivity and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical redundancy is used for sensor fault detection, then fault detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates virtual copies of sensor data through mathematical models. Instead of installing physical redundant sensors, the system generates estimated sensor outputs from vehicle dynamics models and compares these virtual copies with actual sensor measurements to detect faults.
Solution Approach 2:
The patent replaces the mechanical approach of physical sensor redundancy with an analytical/mathematical approach. Fault detection is achieved through model-based estimation and comparison algorithms rather than through additional physical sensor components.
2Measurement precision
If fixed thresholds are used for fault detection, then system simplicity is maintained, but measurement precision and false alarm rate worsen
Solution Approach 1:
The patent transforms static fixed thresholds into dynamic adaptive thresholds that automatically adjust based on vehicle operating conditions. The thresholds are modified in real-time according to parameters such as vehicle speed, steering angle, and road conditions to maintain optimal fault detection precision.
Solution Approach 2:
The patent changes the threshold parameter from a constant value to a variable that adapts to operating conditions. By modifying threshold values dynamically based on vehicle state parameters, the system achieves higher measurement precision without requiring overly complex fixed threshold structures.
3Measurement precision
If adaptive thresholds are implemented, then fault detection precision is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary calculations of adaptive thresholds based on pre-defined relationships with vehicle operating parameters. By establishing threshold adjustment rules in advance and calculating them proactively before fault detection is needed, the system reduces real-time computational burden while maintaining precision.
4Measurement precision
If model-based estimation is used, then fault detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent develops a universal vehicle dynamics model that serves multiple functions: it provides baseline vehicle behavior prediction, generates virtual sensor copies for fault detection, and adapts to different operating conditions. This multi-functional model reduces the need for separate complex subsystems.
Data Source
AI summary
A system and related method for monitoring the state of health of sensors in an integrated vehicle stability control system. In one embodiment, the system determines whether a yaw rate sensor, a lateral acceleration sensor or a hand-wheel angle sensor has failed. The system uses a plurality of models to generate estimates of the outputs of the sensors based on the actual sensor measurements. Residuals are generated as the difference between the measured value and each of the estimates for the particular sensor. The residuals are compared to a threshold to determine whether a fault flag will be set for each residual. The threshold for the hand-wheel angle sensor is an adaptive threshold because it does not have physical redundancy. If the fault flags for the residuals for each sensor have a particular pattern, then a fault is output for that sensor.