Triaxial Acceleration Sensor Self-Adjustment
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Solution Overview
Problem
Existing triaxial acceleration sensors face challenges in self-adjustment during operation due to drift from zero point and sensitivity changes, requiring effort-intensive calibration and being limited by the need for scenario-specific filter adaptations, which do not account for temperature fluctuations and aging.
Innovation Solution
The method employs statistical tests using Kalman filters and error square minimization algorithms to recognize and correct sensor errors, allowing for self-adjustment without end-of-manufacturing process calibration and accounting for external influences like temperature and aging, using estimators to determine sensitivity and offset variances and testing for normal distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional end-of-manufacturing calibration is used, then manufacturing precision is improved, but device complexity and loss of time increase due to additional calibration steps
Solution Approach 1:
The patent performs calibration actions continuously during the sensor's operational life rather than only at manufacturing end. The calibration process is initiated automatically when specific conditions are met (e.g., device held stationary for sufficient time), allowing calibration to occur in advance of any potential drift without requiring dedicated calibration time slots.
Solution Approach 2:
The patent implements continuous calibration capability throughout the sensor's operational life. The calibration process can be executed multiple times at different moments during operation, ensuring that the sensor remains calibrated without requiring a single lengthy calibration session. This continuous approach eliminates the need to stop operations for calibration.
2Measurement precision
If scenario-specific filter adaptations are used, then measurement precision is improved, but device complexity increases due to additional modeling requirements
Solution Approach 1:
The patent employs a universal filtering approach that does not require adaptation to specific product scenarios. The same filtering algorithm can be applied across different applications and scenarios without requiring scenario-specific modeling. This universal approach simplifies the device complexity while maintaining measurement precision through robust statistical methods.
Solution Approach 2:
The patent implements self-adjustment capability where the sensor system automatically detects and corrects its own calibration drift without external intervention. The system monitors its own performance and initiates calibration procedures automatically when drift is detected, eliminating the need for complex external modeling and manual filter adaptation.
3Manufacturing precision
If traditional calibration methods are used, then manufacturing precision is improved, but reliability decreases due to inability to account for temperature fluctuations and aging
Solution Approach 1:
The patent implements feedback mechanisms where the sensor system continuously monitors its own calibration status and adjusts accordingly. The system detects drift conditions and automatically initiates calibration procedures based on real-time performance monitoring, creating a closed-loop system that maintains reliability despite temperature fluctuations and aging.
Solution Approach 2:
The patent transitions from static end-of-manufacturing calibration to dynamic calibration during operation. The calibration process adapts to changing operational conditions including temperature variations and aging effects. The system can perform calibration at multiple time points during the sensor's lifecycle, allowing it to dynamically compensate for drift rather than relying on a single initial calibration.
Data Source
AI summary
A method for self-adjustment of a triaxial acceleration sensor during operation includes: calibrating the sensor; checking the self-adjustment for an interfering acceleration, with the aid of a measurement equation and estimated values for sensitivity and offset; repeating the adjustment if an interfering acceleration is recognized; and accepting the estimated values for sensitivity and offset as calibration values if an interfering acceleration is not recognized. The step of checking the self-adjustment includes: estimating sensitivity and/or offset and the variance thereof; determining an innovation as the difference between a measured value of the measurement equation and an estimated value of the measurement equation; testing the innovation for a normal distribution; and recognizing the interfering acceleration in the event of a deviation from the normal distribution.


