Structural Behavior Monitoring Using Acceleration and Jerk Indicators
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Solution Overview
Problem
Existing methods for detecting structural degradation in structures like bridges using motion detectors and FFT analysis face challenges such as false positives, excessive processing demands, and inability to integrate historical data, leading to reduced confidence and potential safety hazards.
Innovation Solution
The use of aggregated indicators, including Sum of Absolute Values of Acceleration (ASUM), Sum of Absolute Values of Jerk (JSUM), and Jerk Index (JI), calculated locally and transferred efficiently to the cloud for further processing, allowing for real-time detection of structural anomalies and load changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If FFT analysis is applied to acceleration signals for detecting structural degradation, then measurement precision is improved, but processing resources and computational complexity increase exponentially
Solution Approach 1:
The patent segments the acceleration signal analysis by dividing the time series into multiple windows and applying FFT to each window individually. This allows the system to process data in manageable chunks rather than handling the entire signal at once, reducing computational complexity while maintaining detection precision through localized frequency analysis.
Solution Approach 2:
The patent extracts key features from the acceleration signals such as peak frequencies, amplitude values, and temporal patterns before feeding them into the degradation detection algorithm. By extracting only the most informative features rather than processing the complete raw signal, the system reduces processing requirements while preserving measurement precision for degradation detection.
2Measurement precision
If multiple accelerometers are mounted on the structure to improve detection coverage, then measurement precision is improved, but data volume and processing requirements increase
Solution Approach 1:
The patent merges the data from multiple accelerometers by combining their signals into a unified analysis framework. The system integrates information from all sensors to create a comprehensive view of structural behavior, using the combined data to improve detection accuracy while managing data volume through coordinated processing of multiple signal sources.
Solution Approach 2:
The patent implements a universal processing framework that handles data from multiple accelerometers of varying configurations. The system can process signals from different sensor orientations and locations through a common analysis pipeline, enabling flexible deployment of multiple sensors without proportionally increasing processing complexity.
3Speed
If real-time FFT analysis is performed on high-frequency data from multiple accelerometers, then detection speed is improved, but processor capacity requirements increase
Solution Approach 1:
The patent performs preliminary processing of acceleration data by pre-filtering signals, removing obvious noise, and preparing data structures before the actual FFT analysis. This preliminary action reduces the amount of data that requires intensive processing, enabling faster detection speeds with lower processor capacity requirements.
Solution Approach 2:
The patent implements periodic FFT analysis at optimized intervals rather than continuous processing. By analyzing data at strategically determined time periods when structural changes are most likely to occur, the system achieves fast detection capability while reducing overall processor utilization compared to continuous real-time analysis.
4Reliability
If historical data is stored and analyzed using statistical comparisons, then reliability is improved, but storage requirements and processing complexity increase
Solution Approach 1:
The patent extracts and stores only the most critical features from historical data such as peak acceleration values, frequency characteristics, and temporal patterns rather than storing complete raw signal data. This selective storage approach maintains the reliability needed for statistical comparisons and pattern recognition while significantly reducing storage requirements.
Solution Approach 2:
The patent implements a data retention strategy that keeps historical data for a limited period sufficient for statistical analysis and pattern detection, then discards older data. This approach maintains the reliability needed for detecting structural degradation through statistical comparisons while minimizing long-term storage requirements by retaining only the most relevant historical information.
Data Source
AI summary
Systems and methods include reception, for each of a first plurality of consecutive time periods, of an acceleration value associated with a first location of a structure, determination of a first value of a first indicator based on absolute values of the acceleration values, determination of a second value of a second indicator based on absolute values of differences of consecutive one of the acceleration values, determination of a first value of an index based on the first value and the second value, determination of a physical characteristic of the structure based on the first value of the first indicator and the first value of the index, and transmission of an alert based on the physical characteristic.