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 mills 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 Welchs 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.
mill level feature extraction deep belief network back propagation neural network
Muchao Lu Yan Kang Xiaoming Han Gaowei Yan
College of Information Engineering, Taiyuan University of Technology, Taiyuan, 030024
国际会议
长沙
英文
189-193
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）