A bilayer optimization strategy of optimal sensor placement for parameter identification under uncertainty

A bilayer optimization strategy is proposed in this research in order to improve the efficiency in the process of optimal sensor placement aiming at decreasing the uncertainty in identification of parameters. Firstly, the surrogate model between structural parameters and responses is established to improve the solution efficiency of uncertain parameters. Secondly, a particle swarm optimization algorithm based on spatial coordinates is proposed for effective optimal sensor placement. Finally, this research proposes an efficient solution strategy for optimal sensor placement with uncertainty, i.e., the proposed coordinate-based particle swarm optimization method is utilized for outer layer optimization, and surrogate model is used to solve the interval boundaries of structural parameters as an inner layer optimization method. The optimization results aiming at redundancy index of rectangular plate based on the proposed optimization algorithm and existing algorithms are compared. The mean value of optimization results of proposed method is 29.7% higher than the mean value of optimization results of GA. The proposed optimization strategy is verified by numerical example and an experimental work. The results of single objective optimization and multi-objective optimization are given, respectively. The computational efficiencies of the traditional method and the proposed optimization method are compared. The optimization efficiency of the proposed optimization method is four orders of magnitude higher than that of the traditional method. The proposed strategy provides a feasible idea for improving the efficiency of large-scale sensor layout optimization under uncertainty.