The Modeling Method of a Vibrating Screen Efficiency Prediction Based on KPCA and LS-SVM

Abstract

A vibrating screen efficiency prediction modeling method based on autoregressive (AR) model and least square support vector machine (LS-SVM) was proposed. The vibration signals of a self-synchronized vibrating screen were collected to establish the AR model. Nonlinear principal components of the signals were extracted by the kernel principal component analysis (KPCA), followed by the regression model reconstruction using LS-SVM to accomplish reduced complexity of the prediction model from AR coefficients and improved generalization capacity and learning speed. The results show that the model predictions are consistent with the experimental data, which indicates that the modeling method is applicable and feasible in adjusting the design and process parameters of vibrating screens. Furthermore, the work condition monitoring method in the experiment is feasible for faults diagnosis of mechanical equipment.