Magnetic Sensor Error Detection via Multi-Channel Comparison
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Solution Overview
Problem
Magnetic sensors in applications like automotive steering systems face challenges due to changing sensitivity levels and non-linearity errors caused by temperature changes, necessitating an effective sensor error detection mechanism.
Innovation Solution
The implementation of a system comprising multiple magnetic sensing channels with processors that compare expected data from synchronized sensor outputs to detect errors, utilizing magnetoresistive sensors and differential amplifiers to provide error flags when thresholds are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If magnetic sensing elements are used to obtain position or angle information, then the sensor can provide measurement data, but the sensor suffers from changing sensitivity levels and non-linearity errors due to temperature change
Solution Approach 1:
The sensor system is divided into multiple independent sensing channels (first channel with first magnetic sensing element, second channel with second magnetic sensing element, third channel with third magnetic sensing element), each measuring the same physical quantity but through separate pathways. This segmentation allows individual error detection and comparison without compromising the overall measurement function.
Solution Approach 2:
The system implements a feedback mechanism where the processor compares data from multiple sensing channels and generates an error flag when discrepancies exceed a threshold. This feedback loop continuously monitors sensor health and provides real-time error detection, allowing the system to respond to temperature-induced variations and maintain measurement reliability.
2Reliability
If multiple sensing channels are implemented for error detection, then sensor error detection capability is improved, but device complexity increases
Solution Approach 1:
The sensor system performs self-diagnosis by using its own multiple sensing channels to detect errors in each other. The processor compares data from the first, second, and third channels and autonomously determines when an error has occurred, eliminating the need for external monitoring systems and reducing overall system complexity.
Solution Approach 2:
The system combines multiple sensing channels into a unified processing architecture where a single processor handles data from all channels and performs error detection. This merging approach consolidates the complexity into a centralized unit rather than requiring separate error detection circuits for each channel, thereby managing device complexity more efficiently.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables robust detection of sensor errors, ensuring accurate angle and radius measurements in magnetic sensors, thereby enhancing the reliability of systems like Electric Power Assisted Steering systems by self-checking for temperature-induced variations.
Implementation Method 1
Each of the first and second sensing elements can be a magnetoresistive sensor. For instance, the first and second sensing elements can be anisotropic magnetoresistance (AMR) sensors, giant magnetoresistive (GMR) sensors, or tunneling magnetoresistive (TMR) sensors.
Data Source
AI summary
Sensor error detection with an additional channel is disclosed herein. First and second magnetic sensing elements can be disposed at angles relative to each other. In some embodiments, the first and second magnetic sensing elements can be magnetoresistive sensing elements, such as anisotropic magnetoresistance (AMR) sensing elements. Sensor data from first and second channels, respectively, having the first and second sensing elements, can be obtained. Third channel can receive a signal from the first sensing element and a signal from the second sensing element, and sensor data from the third channel can be obtained. Expected third channel data can be determined and compared to the obtained third channel data to indicate error.