Soft sensor modeling of ball mill load via principal component analysis and support vector machines

Abstract

In wet ball mill, measurement accuracy of mill load (ML) is very important. It affects production capacity and energy efficiency. A soft sensor method is proposed to estimate the mill load in this paper. Vibration signal of mill shell in time domain is first transformed into power spectral density (PSD) using fast Fourier transform (FFT), such that the relative amplitudes of different frequencies contain mill load information directly. Feature variables at low, medium and high frequency bands are extracted through principal component analysis (PCA), which selects input as a preprocessing procedure to improve the modeling performance. Three support vector machine (SVM) models are built to predict the mill operating parameters. A case study shows that proposed soft sensor method has higher accuracy and better predictive performance than the other normal approaches. © 2010 Springer-Verlag Berlin Heidelberg.