Parameter identification of nonlinear rotor-bearing system based on improved kriging surrogate model

Abstract

This paper presents a new method which can identify the structure parameters (such as the bearing parameters, the nonlinear rub-impact parameters, and so on) of a nonlinear rotor-bearing system. Based on an improved kriging surrogate model and evolutionary algorithm (IKSMEA), the new method can provide more accurate results with less computation time. The initial kriging surrogate model (KSM) is constructed by the samples of varying structure parameters and their response values. According to the identified process, a multi-point addition criterion is proposed and more appropriate predicted points are added to update the KSM. Numerical studies and experimental validation of a nonlinear rotor-bearing system are performed. Comparing to the previous method (KSM and evolutionary algorithm), the new method satisfies the condition of convergence with less updating steps and increased robustness to noise. The identified results indicate that the IKSMEA can identify the nonlinear rotor system more effectively and accurately.