Spectral Kernel Principal Component Selection Based on Empirical Mode Decomposition and Genetic Algorithm for Modeling Parameters of Ball Mill Load

Abstract

Parameters of ball mill load (ML) affects production capacity and energy consumption of the grinding process, which have stronger correlation with shell vibration spectrum. A novel spectral features extraction and selection approach combined with empirical mode decomposition(EMD), power spectral density(PSD), kernel principal component analysis(KPCA), genetic algorithms(GA) and partial least square(PLS) was proposed in this paper. At first, shell vibration signals were decomposed into a number of intrinsic mode functions (IMFs) based on the EMD. Secondly, the PSD of each IMF was obtained. At last, the mainly spectral KPCs extracted from the PSD were integrated together as the candidate features set. GA was used to optimize spectral KPCs as the selected features subset, which was used to construct ML parameters soft sensor models based on PLS algorithm. The experimental result shows that the proposed approach has higher accuracy and better predictive performance than other normal approaches.