Development of NVW-SAEs with nonlinear correlation metrics for quality-relevant feature learning in process data modeling

Abstract

Abstract Soft sensors have become reliable tools for predicting difficult-to-measure quality variables in modern industrial process modeling. Feature representation is a key step to construct accurate soft sensor models. In the past decade, deep learning has shown great capacity of feature extractor for soft sensor modeling. However, most existing deep networks cannot capture quality-related features for output prediction. To deal with this problem, a variable-wise weighted stacked autoencoder (VW-SAE) was previously proposed to learn deep quality-related features, in which a variable weighted objective function is designed to learn quality-related features layer by layer. However, only linear correlation is considered for variable weighting, which is insufficient to extract quality-related features fully. In this paper, nonlinear VW-SAEs (NVW-SAEs) are constructed to enhance the learning capability for deep quality-related features, in which three correlation metrics are utilized to measure the nonlinear variable relationships and learn deep quality-related features. The prediction results show that NVW-SAEs can effectively extract quality-related features from process data. Two public data sets and an industrial debutanizer are used to validate the effectiveness of the NVW-SAEs.