State Recognition of Mill Load Based on Improved K-means Clustering Algorithm

Abstract

Abstract In this work, a new identification method for the load identification of ball mill based on improved K-means clustering is proposed. Since the external device response signals of ball mill are closely related to its load during the operation, the appropriate parameters are selected. The number of clusters and initial clustering centers are meliorated based on their density and genetic algorithm. The dimensionality of input features are reduced with kernel principal component analysis (KPCA), and the final clustering centers are obtained by iterating in the space with reduced dimensionalities. Tests on datasets have demonstrated the higher accuracy and stability of our improved K-means method compared with other methods. The case-analysis results show that the mill load status can be identified clearly according to the displacement variations of cluster centers calculated by this method. Our research provides a new reference for mill load identification in the viewpoint of multi-source information fusion.