Bridge Superstructure Displacement Measurement Using Acceleration Data
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Solution Overview
Problem
Existing displacement acquisition devices for bridges struggle with accuracy due to insufficient approximability between static components and stored data, leading to decreased measurement accuracy and increased complexity in data management, especially when environmental changes occur.
Innovation Solution
A measurement method involving low-pass and high-pass filter processing to reduce vibration and drift noise, followed by correction data estimation and generation of measurement data by inverting signal signs in specific intervals, allowing for accurate removal of noise without pre-existing error reduction information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static component data is stored for each classification of railway vehicle and bridge to improve measurement accuracy, then measurement precision is improved, but device complexity increases and cost increases
Solution Approach 1:
The patent extracts and removes the static component from the measured displacement signal through signal processing techniques. By separating the static component (which contains approximation errors) from the dynamic component, the system eliminates the need to store and manage static component data for different vehicle and bridge classifications, thereby reducing device complexity while maintaining measurement accuracy.
Solution Approach 2:
The system performs self-correction by automatically detecting and removing drift noise and static components from the measured signal in real-time. This self-service approach eliminates the need for external calibration data or pre-stored static component information, simplifying the overall system configuration and reducing costs.
2Device complexity
If static component approximation is used to simplify data management, then device complexity is reduced, but measurement precision decreases when approximability is insufficient
Solution Approach 1:
The patent implements feedback mechanisms through acceleration sensors that continuously monitor the bridge structure. The measured acceleration data is processed to detect drift noise and static components, and correction values are fed back to compensate for approximation errors in real-time, maintaining high measurement precision without requiring complex pre-stored data.
Solution Approach 2:
The system dynamically adjusts processing parameters such as filter cutoff frequencies and integration time constants based on the detected signal characteristics. This allows the system to adapt to different measurement conditions and maintain high precision across various scenarios without requiring separate static component data for each condition.
3Measurement precision
If drift noise removal is performed using high-pass filter processing, then measurement precision is improved, but loss of information occurs in the low-frequency component
Solution Approach 1:
The patent segments the displacement signal into distinct frequency components: static component (very low frequency), drift noise (low frequency), and dynamic component (higher frequency). By applying different processing techniques to each segment - such as differentiation for static component removal and selective filtering for drift noise - the system preserves important low-frequency information while removing unwanted drift.
Solution Approach 2:
The system uses a composite approach combining multiple signal processing techniques: differentiation, integration, high-pass filtering, and acceleration-based correction. This composite methodology allows the system to remove drift noise effectively while preserving low-frequency signal information that would be lost with simple high-pass filtering alone.
Data Source
AI summary
A measurement method includes: performing low-pass filter processing and high-pass filter processing on target data; estimating correction data; generating vibration component data, and the estimating the correction data includes: specifying a first interval, a second interval, and a third interval; generating first interval correction data, generating second interval correction data in the second interval by setting data in an interval before a first intersection point of first line data and second line data as the first line data, setting data in an interval from the first intersection point to a second intersection point of second line data and third line data as the second line data, and setting data in an interval after the second intersection point as the third line data; and generating third interval correction data.