Novel Deep Layers-Extended Autoencoder With Correlation and Its Industrial Soft Sensing

Abstract

Due to the increasing complexity of industrial processes, accurately constructing soft sensors has become a daunting challenge. Recently, stacked autoencoder (SAE) has been utilized to develop data-driven soft sensors due to their superior capability in feature extraction. However, the conventional SAE lacks correlation in feature extraction for each autoencoder (AE) as it is independent. Additionally, the training process of AE is unsupervised, ignoring the correlation between input and output data. To address these issues, a novel SAE with a variant structure called deep layers-extended autoencoder (DLEAE) is proposed. In the DLEAE, an extended layer is introduced, and the original inputs and the information of the hidden layers of previous LEAE (Layers-extended autoencoder) units are added to the follow-up LEAE unit. This extends the width of DLEAE and strengthens the utilization of the features extracted from preceding layers. Furthermore, the expected outputs are introduced into the feature extraction process of each LEAE unit, which filters out output-irrelevant information. Finally, experimental verifications using two actual complex industrial processes are performed to validate the effectiveness and superiority of the proposed DLEAE.