Deep learning with nonlocal and local structure preserving stacked autoencoder for soft sensor in industrial processes

Abstract

Abstract Deep learning-based soft sensor has been widely used for quality prediction in modern industry. Traditional deep learning like stacked autoencoder (SAE) only captures the feature representations by minimizing the global reconstruction errors, which causes a loss of the intrinsic geometric structure embedded in the raw data. To address this problem, a nonlocal and local structure preserving stacked autoencoder (NLSP-SAE) is proposed for soft sensor. Different from the original SAE, NLSP-SAE aims to extract the meaningful structure-relevant features by establishing a new objective function with a regularizer of the nonlocal and local data structure information. For local structure preserving, NLSP-SAE enforces two adjacent data points to be near each other in the reconstructed space. While for nonlocal structure preserving, NLSP-SAE constrains two nonadjacent data points to be far apart from each other. The application on an industrial hydrocracking process demonstrates that NLSP-SAE can improve the prediction accuracy for quality variables.