Stacked locality preserving autoencoder for feature extraction and its application for industrial process data modeling

Abstract

Deep learning has recently caught much attention in the industrial processes, particularly for soft sensor applications. However, most traditional deep learning networks cannot extract local features for data modeling. To overcome this problem, a novel stacked locality preserving autoencoder (S-LPAE) is proposed in this paper. First, the neighborhood topological structure is built for the historical samples and the weights between the neighbor samples are calculated. Then, locality preserving autoencoder (LPAE) is designed to minimize both the reconstruction error and the additional local preserving constraint of the training dataset, with which the potential features can better preserve the local data structure. After that, multiple LPAE modules are sequentially stacked to construct the S-LPAE network to obtain deep locality-preserving features. Finally, the extracted features are directly used for the output prediction of soft sensor. To validate the performance of the proposed algorithm, it is applied to an industrial hydrocracking process to predict the 90% boiling point of aviation kerosene and the 50% boiling point of diesel.