Hybrid Sensor Placement Framework Using Criterion-Guided Candidate Selection and Optimization

Area of Science:

• Structural Health Monitoring
• Computational Mechanics
• Sensor Networks

Background:

• Optimal sensor placement is crucial for effective Structural Health Monitoring (SHM).
• Traditional methods often struggle with sparse data and accurate response reconstruction.
• Incorporating physical constraints can improve the reliability of SHM systems.

Purpose of the Study:

• To develop a hybrid methodology for optimal sensor placement in SHM.
• To integrate criterion-based candidate selection with advanced optimization algorithms.
• To enhance the accuracy of structural response reconstruction from limited sensor data using constrained dynamics.

Main Methods:

• A hybrid approach combining four selection criteria (MKE, MSE, MAC sensitivity, MI) for candidate DOF selection.
• Application of four optimization algorithms (greedy, GA, PSO, SA) to identify optimal sensor subsets.
• Utilizing the Udwadia-Kalaba (U-K) generalized inverse for constrained dynamic response expansion from sparse data.
• Validation through Monte Carlo simulations under various noise levels.

Main Results:

• The proposed hybrid methodology effectively identifies optimal sensor locations.
• The Udwadia-Kalaba generalized inverse enables accurate and physically consistent reconstruction of unmeasured responses.
• The framework demonstrates robustness against noise through extensive simulations.
• The U-K method provides improved numerical conditioning and stability compared to conventional pseudo-inverses.

Conclusions:

• The hybrid sensor placement methodology offers a flexible and effective solution for data-driven sensor deployment in SHM.
• The integration of U-K generalized inverse significantly enhances the accuracy and reliability of structural response reconstruction.
• This approach is particularly beneficial for underdetermined or ill-posed problems in SHM.