Multivariate Deep Reconstruction Neural Network for Multi-step-ahead Prediction of Industrial Process Quality Variables

Abstract

In industrial processes, deep learning has been widely used to solve the soft sensing problem. Multi-step-ahead prediction is one of the most challenging problems in the field of soft sensors. Recently, N-Beats has been proposed as a promising deep neural architecture for multi-step prediction, but it can only be used for univariate time series prediction, not for industrial soft sensor modeling. Inspired by N-Beats, a novel deep learning model, multivariate deep reconstruction neural network (MDRNN), is proposed for multivariate time series prediction in this work. MDRNN is designed on the basis of a doubly residual structure with a deep stack of fully-connect layers. MDRNN inherits the merits of N-Beats and incorporates the “doubly residual stacking” idea into the industrial soft sensor modeling to improve the prediction accuracy. To evaluate the feasibility and effectiveness of the proposed MDRNN, it is applied to the quality prediction task and validated with two real-world industrial processes. The application results demonstrated that the proposed MDRNN can achieve higher prediction accuracy compared to the existing N-Beats and Multilayer Perceptron (MLP)-based methods.