Identification of the error excitation in gear systems: A mediator algorithm between simulation and experiment

There are three primary sources of internal excitation in gear systems: time-varying mesh stiffness, meshing damping and tooth error. Compared with the first two excitations, it is more difficult to evaluate or measure the error excitation. The error excitation is usually assumed empirically or even neglected in the majority of dynamic models. To address this issue, we desired to propose a mediator algorithm between the simulation and the experiment to identify the error excitation of gear systems. Based on the second-order cyclostationarity of gear signals, a signal processing procedure is proposed to acquire the mediator signal with a high signal-to-noise ratio. The mediator signal is served as the media between the simulation and experiment. The error parameter is updated repeatedly in the iterative optimization until the optimal matching is found between the simulation and the experiment. Simultaneously, the pre-decomposition technique is proposed to accelerate the solution of the simulation dynamic model. The performance of the proposed identification algorithm is verified both numerically and experimentally. Moreover, noise immunity analysis shows that the proposed identification algorithm is robust to noise, damping and phase differences between the experiment and simulation. The proposed identifi-cation algorithm provides an indirect approach to estimating the error excitation when the direct measurement is unavailable.

Automated summary

The article introduces the three primary sources of internal excitation in gear systems and proposes a mediator algorithm to identify the error excitation. By utilizing signal processing and iterative optimization, the algorithm achieves optimal matching between simulation and experiment. Additionally, it proposes a pre-decomposition technique to accelerate the solution of the simulation dynamic model, and the performance of the algorithm is verified numerically and experimentally, showing robustness to noise, damping, and phase differences.