A novel soft sensor model for ball mill fill level using deep belief network and support vector machine

Abstract

Effective feature extraction provides multifarious benefits such as improved accuracy and reliability for soft sensor. Based on deep belief network (DBN) and support vector machine (SVM), a novel soft sensor approach is proposed in this paper to solve the problem of measurement of fill level inside the ball mill. This measurement methodology of ball mill fill level using the DBN based soft sensor can be structured in two consecutive stages: first, DBN is employed to construct a deep architecture to obtain the high level representation of the vibration frequency spectrum of the ball mill bearing; second, SVM is then trained to model the relationship between the learned deep features and fill level. The effectiveness of the proposed approach can be clearly seen by comparing with other methods based on traditional feature extraction algorithms and machine learning algorithms. Experimental results prove that the model based on DBN and SVM performs effectively, especially in the condition with a few labelled samples.