Soft sensor modeling of mill level based on Deep Belief Network

Accurate measurement of the mill level is a key factor to improve the ball mill's productive efficiency, safety and economy. Aiming at solving the critical problem of the mill level soft sensor, feature extraction of the processing parameters, a novel method based on Deep Belief Network (DBN) is proposed. DBN is one of the deep learning methods, which focuses on learning deep hierarchical models of data. In this paper, basic features, namely power spectrum density are obtained from the vibration signal of ball mill by Welch's method firstly. Then DBN is built on the basic features to learn high level deep features. Finally a supervised learning algorithm named back propagation neural network is used to model the relationships between extracted features and mill level. Experimental results indicate that the DBN based method outperforms traditional feature extraction methods.