Feature Reconstruction-Regression Network: A Light-Weight Deep Neural Network for Performance Monitoring in the Froth Flotation

Abstract

With the rapid development of deep neural network (DNN), many DNN-based models for performance monitoring have been developed recently. However, some challenges still exist in the industrial performance monitoring: 1) different sample rates and time delays between the inputs and labeled performance; 2) a light-weight DNN architecture. Under this circumstance, we design a DNN named feature reconstruction-regression network (FR-R net) in this article. First, we extract the feature vector series as the input feature in order to capture the dynamic temporal information of the input data. Then, we design a feature reconstruction network with a weight-shared kernel network and fixed positional encoding to generate a reconstructed feature vector. Finally, we send the reconstructed feature vector into fully connected layers as a regression network to link the labeled performance. The effectiveness of the proposed FR-R net is validated on both a simulation case and an industrial froth flotation process.