Load State Identification Method for Wet Ball Mills Based on the MEEMD Singular Value Entropy and PNN Classification

Abstract

To overcome the difficulty of accurately judging the load state of a wet ball mill during the grinding process, a method of mill load identification based on the singular value entropy of the modified ensemble empirical mode decomposition (MEEMD) and a probabilistic neural network (PNN) classifier is proposed. First, the MEEMD algorithm is used to decompose the vibration signals recorded under different load states to obtain the intrinsic mode components, and a correlation coefficient threshold is used to select the sensitive mode components that characterize the state of the original signal. Second, singular value decomposition is used to obtain the singular value entropy. Finally, the load state of the wet ball mill is judged based on the magnitude of the singular value entropy. A characteristic mill load vector is constructed from the singular value entropies of the cylinder vibration signals recorded under different load conditions and is used as the input to a PNN, which then outputs the predicted ball mill load state; in this way, a load state identification model is established. Grinding experiments are presented to verify the effectiveness of the proposed method, showing that the method can accurately identify the load state of a wet ball mill.