Bearing parameter identification of rotor-bearing system based on Kriging surrogate model and evolutionary algorithm

Bearing dynamic parameters are important factors governing the vibration characteristics of rotating machinery, but they are usually unknown in the modeling. In this paper, an effective method is proposed to identify the bearing parameters and unbalance information of a rotor-bearing system based on the Kriging surrogate model and evolutionary algorithm (KSMEA). The initial Kriging surrogate model is constructed by the samples of various identification parameters (bearing parameters and magnitude of mass unbalance) and measured unbalance responses, which substitutes the original finite element model. It effectively reduces the computational expense of identification. In order to search for the global optimal solution exactly, one of the evolutionary algorithms, differential evolution (DE) algorithm is employed based on the constructed Kriging surrogate model. The effect on different numbers of samples is discussed to improve the accuracy of the Kriging surrogate model. Both numerical example and experimental results indicate that the proposed method can identify the bearing parameters and unbalance information of rotor-bearing system accurately and reliably.