Papers by Author: Rui Xue Liu

Abstract: During identify natural frequency of bearing rotor, due to the complex non-linear relationship among the factors which influence natural frequency, so it is hard to establish a complete and accurate theoretical model. Based on the generalization and approximation of non-linear mapping capability of support vector machine (SVM) and the powerful ability of global optimization of the genetic algorithm (GA), the paper through optimizing the SVM by GA, establishes combined Genetic Support Vector Machine (GA-SVM). The method establishes the mapping between the natural frequency of a rolling bearing rotor and the various parameters, which reduces the rotor structure for the study similar to the natural frequency of the calculation of the workload greatly. Using the model to indentify the natural frequency of bearing rotor under different parameters, then compare identification value with experimental values shows that projections in good agreement with the experimental data.

Abstract: During identifying the natural frequency of the rolling bearing rotor system, due to the complex non-linear relationship between the factors which influence the natural frequency, it is hard to establish a complete and accurate theoretical model. Based on the self-learning ability and approximation of non-linear mapping capability of the artificial neural network (ANN) and the powerful ability of global optimization of the genetic algorithm (GA), the paper establishes combined genetic neural network (GA–ANN) through optimizing the ANN by GA. This method establishes the mapping between a rolling bearing rotor system natural frequency and the various parameters, which reduces the calculation of the workload greatly for the study of the similar rotor structure’s natural frequency. Through using the network model to predict the natural frequency of rolling bearing rotor system under different parameters, we finally find that the predicted values are in good agreement with the experimental data, which indicates that the method is powerful in identification.