Novel Autoencoder Based on Variable Correlation Analysis for Industrial Soft Sensing

Abstract

In today's industrial processes, data-driven soft sensors are a frequently used tool for predicting quality variables. Autoencoder (AE) is an unsupervised algorithm which can extract latent features from initial data. However, during the feature extraction process, the traditional autoencoder does not consider the correlation between modeling input variables and quality variables to be predicted. To solve this issue, a novel autoencoder based on variable correlation analysis (VCA-AE) is proposed. In VCA-AE, the correlation of modeling input variables and quality variables to be predicted is performed by correlation analysis, and input variables are divided into two parts, which are input to the sub-autoencoder to extract latent features, respectively. In each sub-autoencoder, input variables and quality variables have the same correlation. Next, a feedforward neural network Extreme Learning Machine (ELM) is used to develop soft sensor model based on the extracted latent feature variables and quality variables. Finally, the effectiveness of the proposed soft sensor model combining VCA-AE and ELM is illustrated by an experiment of the industrial PTA process.