Uncorrelated multi-source random dynamic load identification based on minimization maximum relative errors and genetic algorithm

Abstract

Due to the ill-conditioned inverse characteristics of uncorrelated multi-source random dynamic load identification problem, there are large condition number and large identification errors for classic least-squares of generalization method at inherent natural frequencies. In order to avoid its illness and singularity, this multi-objective optimization inverse problem is turned into single-objective optimization forward problem by criterion function of minimization maximum relative errors of all response measuring points, and we adopt genetic algorithm to search this optimal solution then. Results of uncorrelated multi-source vibration load identification on cylindrical shell CAE simulation data set show that this new method is much better in precision and is less sensitive for measurement noise than classic least-squares of generalization method.