LAUSR

Abstract Deep learning has been growing in popularity for soft sensor modeling of nonlinear industrial processes, infeuality-related variables. However, applications may be highly nonlinear, and the quantity of labeled samples is considerably limited. The extraction of relevant information from abundant unlabeled data is becoming an area of increasing interest in soft-sensor development. A novel ensemble deep relevant learning soft sensor (EDRLSS) modeling framework based on stacked autoencoder (SAE), mutual information (MI), and bagging-based strategy is proposed. SAE is trained layer-by-layer with MI analysis conducted between targeted outputs and learned hidden representations to evaluate and weight the current layer representations. The proposed method eliminates irrelevant information and weights the retained features to highlight the most relevant representations. Thus, the approach extracts deep representative information. Besides, a bagging-based ensemble strategy is applied to improve the soft-sensor performance and reliability. Two real-world industrial nonlinear processes are used to evaluate the EDRLSS framework performance. The results show enhanced prediction performance compared to other state-of-the-art and traditional methods.