Ball Mill Load State Recognition Based on Kernel PCA and Probabilistic PLS-ELM

Abstract

Operating condition recognition of ball mill load is important to improve product quality, decrease energy consumption and ensure the safety of grinding process. A probabilistic one-against-one (OAO) multi-classification method using partial least square-based extreme learning machine algorithm (PLS-ELM) is proposed to identify the operating state of ball mill. The feature of shell vibration spectrum is extracted using KPCA. PLS-ELM model is applied to enhance the reliability and accuracy of the operating conditions identification of the ball mill load. Posterior probability of each class using Bayesian decision theory is defined as a measure as classification reliability. Classification results of the experimental ball mill shown that the accuracy and stability of the proposed method outperform ELM, PLS-ELM and KPCA-ELM model.