Optimal sensor placement based on dynamic condensation using multi-objective optimization algorithm

Based on the effective independence method and dynamic condensation approach, a sensor placement method is proposed and solved using a modified NSGA-II in this paper, which is evaluated by a novel distribution index. Based on the relationship between the optimal sensor placement in modal identification and the choice of master degrees of freedom in dynamic condensation, this study aims to realize the multi-objective optimization of position selections that connect the aforementioned research fields. Two objectives are constituted based on the determinant of the Fisher information matrix in the effective independence method and the condensation accuracy of the reduced model in a dynamic reduction approach, which comprises the multi-objective optimal sensor placement problem. A novel distribution index to appraise multi-objective non-dominated solutions is investigated to assess the suitability of multi-objective optimization solutions based on the Pareto front distributions. Furthermore, based on this novel index, a modified NSGA-II is constructed by updating the process to enhance the efficiency of the proposed optimal sensor placement method. Finally, two numerical examples are used to verify the effectiveness and accuracy of the proposed method, along with a comprehensive discussion.