A Multiple-Kernel Relevance Vector Machine with Nonlinear Decreasing Inertia Weight PSO for State Prediction of Bearing

Abstract

The scientific and accurate prediction for state of bearing is the key to ensure its safe operation. A multiple-kernel relevance vector machine (MkRVM) including RBF kernel and polynomial kernel is proposed for state prediction of bearing in this study; the proportions of RBF kernel and polynomial kernel are determined by a controlled parameter. As the selection of the parameters of the kernel functions and the controlled parameter has a certain influence on the prediction results of MkRVM, nonlinear decreasing inertia weight PSO (NDIWPSO) is used to select its kernel parameters and controlled parameter. The RBF kernel RVM model with NDIWPSO (NDIWPSO-RBFRVM) and the polynomial kernel RVM model with NDIWPSO (NDIWPSO-PolyRVM) are used, respectively, to compare with the multiple-kernel RVM model with NDIWPSO (NDIWPSO-MkRVM). The experimental results indicate that NDIWPSO-MkRVM is more suitable for the state prediction of bearing than NDIWPSO-RBFRVM and NDIWPSO-PolyRVM.