Adaptive Sparse Sensor Placement for Structural Health Monitoring
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Solution Overview
Problem
Existing sensor arrangement methods for structural health monitoring in urban transportation infrastructure face challenges such as local optima, low optimization efficiency, and difficulty in ensuring accuracy and convergence, leading to inefficiencies and resource waste.
Innovation Solution
A method for arranging sparse sensors using an adaptive basis matrix and compressed sensing to determine an optimal sparse measurement matrix, which involves extracting prior information, constructing an optimal sparse measurement matrix, and determining a sensor arrangement strategy based on this matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of sensors are arranged to monitor complex structures, then measurement precision and reliability improve, but device complexity and cost increase
Solution Approach 1:
The patent extracts only the most critical measurement information from the complex structural response data by identifying dominant modes and extracting key features. This allows a sparse sensor network to capture essential structural health information without requiring comprehensive coverage that would necessitate numerous sensors, thereby reducing device complexity while maintaining measurement precision for critical parameters.
Solution Approach 2:
The patent transforms the monitoring approach by changing from direct measurement of all structural parameters to measuring a reduced set of transformed parameters (modal parameters, curvature modes, or other processed features). This parameter transformation enables accurate structural health monitoring with fewer sensors by focusing on the most informative parameters that capture essential structural behavior.
2Device complexity
If sensors are arranged in limited positions due to practical constraints, then device complexity is reduced, but measurement precision and coverage deteriorate
Solution Approach 1:
The patent performs preliminary analysis of the structural system to identify optimal sensor placement positions and the most informative measurement locations before actual deployment. By pre-determining the critical measurement points and dominant structural modes, the system ensures that sensors placed in limited positions capture the most valuable information, thereby maintaining measurement precision despite the simplified sensor arrangement.
Solution Approach 2:
The patent compensates for limited spatial sensor coverage by transforming the problem into a different dimensional space through modal analysis, frequency domain transformation, or other mathematical operations. This dimensionality change allows the system to extract comprehensive structural information from measurements taken at fewer physical locations, effectively overcoming the limitations of sparse sensor placement.
3Ease of operation
If traditional optimization methods are used for sensor arrangement, then ease of operation is maintained, but optimization efficiency and convergence accuracy deteriorate
Solution Approach 1:
The patent performs preliminary identification of dominant structural modes and key measurement directions before executing the sensor arrangement optimization. This preliminary analysis provides a focused search space and initial conditions for the optimization algorithm, enabling faster convergence to optimal or near-optimal sensor configurations while maintaining computational simplicity and ease of operation.
Data Source
AI summary
Provided are a method and an apparatus for arranging a sparse sensor embedded with physical information, and a device. The method includes obtaining prior information of a target structure and extracting an adaptive basis matrix of the prior information; constructing a problem of an optimal sparse measurement matrix by using a compressed sensing method based on the adaptive basis matrix, and solving the problem of the optimal sparse measurement matrix to obtain the optimal sparse measurement matrix; and determining, based on the optimal sparse measurement matrix, a sensor arrangement strategy corresponding to the target structure, and arranging a sensor for the target structure based on the sensor arrangement strategy. By using the compressed sensing method to determine the optimal sparse measurement matrix, data collected by each sensor has a greatest correlation for solving a specific problem.