An adaptive hybrid differential evolutionary algorithm for the parameter identification of rotating machinery

Abstract

An adaptive hybrid differential evolution algorithm for the dynamic parameter identification of rotating machinery is developed in this paper. Genetic algorithm (GA) is first used to reduce the range of the identification parameters to save computational time. Adaptive Cauchy and Gaussian mutations are then applied in a differential evolution algorithm (DEA) and dynamically updated during the evolutionary process according to the identification process, to obtain the global optimal solution more easily and more rapidly. Numerical studies of linear and nonlinear rotor-bearing systems are performed. The results indicate that the genetic algorithm-adaptive hybrid differential evolution algorithm (GA-AHDE) is more effective at identifying the dynamic parameters and faults of the rotor-bearing system compared to other evolutionary algorithms.