研究目的
To estimate structural shape using a minimal number of fiber Bragg grating sensors by deriving a strain to displacement transformation matrix from mode shapes and optimizing sensor layout with genetic algorithm.
研究成果
The structural shape estimation method using mode shapes and genetic algorithm for sensor optimization is effective, with good agreement between estimated and measured displacements in both static and dynamic tests. Estimation errors are lower at resonance frequencies, and the approach is suitable for engineering applications with restricted sensor numbers.
研究不足
The method shows higher estimation errors for off-resonance frequencies due to the influence of higher-order modes not captured by the limited number of modes used. The dynamic experiment is limited to measuring displacement at only one point due to equipment constraints.
1：Experimental Design and Method Selection:
The study uses a modal approach for shape estimation, deriving a strain to displacement transformation matrix from mode shapes. Genetic algorithm is employed to optimize the number and layout of sensors.
2：Sample Selection and Data Sources:
A cantilever aluminum plate (700mm x 700mm x
3：5mm, aircraft grade Aluminum alloy 2014A-T4) is used for simulations and experiments. Strain data is measured using fiber Bragg grating sensors. List of Experimental Equipment and Materials:
Equipment includes a four-channel interrogator (Micron Optics, Sm-130), coordinate measuring machine (Leitz PMM 12106), modal shaker, noncontact laser displacement sensor, and FBG sensors. Materials include the aluminum plate.
4：Experimental Procedures and Operational Workflow:
Simulations are conducted using finite element analysis with MSC Patran. Static experiments involve symmetrical and unsymmetrical bending, with displacements measured by CMM. Dynamic experiments use a modal shaker for excitation and a laser sensor for displacement measurement. Strain data is acquired and processed to estimate displacements.
5：Data Analysis Methods:
Data is analyzed using root mean square error calculations to compare estimated and measured displacements. Genetic algorithm optimizes sensor layout by minimizing this error.
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