Novel Deep Learning Based on Data Fusion Integrating Correlation Analysis for Soft Sensor Modeling
Hao Wu, Yongming Han, Jian-Yu Jin, Zhiqiang Geng
Abstract
Accurate soft sensing modeling of complex industrial processes can provide operation guidance for improving the product quality. However, most modeling methods cannot mine the process data sufficiently, which leads to low prediction accuracy and generalization performance. Therefore, a novel soft sensing method based on dilated convolution neural network (DCNN) combining data fusion and correlation analysis is proposed. The fused data can be obtained by the sliding window approach, with window sizes of 1 day, to eliminate noise caused by uncertain working conditions. Then, the correlation analysis method is used to analyze the relevance of the fused data to reduce redundant variables. Moreover, the processed variables and target values are taken as inputs and outputs of the DCNN to build the soft sensing model. Finally, the proposed method is applied in a polypropylene production system to predict the melt index. Compared with the extreme learning machine, the convolution neural network, the DCNN, the DCNN based on data fusion, and the DCNN based on correlation analysis, the proposed soft sensing method has achieved state-of-the-art prediction accuracy and generalization ability, which can improve the production efficiency.