During the process of mineral grinding, mill load parameters (MLPs) determine the information of the mechanical signals. So, online MLPs detection is one of the key factors for improving the production efficiency of mineral processing plants. In this paper, the correlation between the multichannel mechanical signal and the different MLPs is explored by the power spectral density. Furthermore, the contribution rate of the multisource and multicomponent mechanical signals to the MLPs and mill load is measured on the basis of the correlation coefficient. Finally, a prediction model for MLPs can be constructed according to an adaptive decomposition strategy and the appropriate sub-signals.
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