Attribute-relevant distributed variational autoencoder integrated with LSTM for dynamic industrial soft sensing

Abstract

Due to the complicated features of process data in terms of high dimension, nonlinearity and coupling, it tends to be a grand challenge to extract important features from complicated process for predicting quality variables using a reliable soft sensor model. Variational Autoencoder (VAE), one of the unsupervised deep learning methods, has been widely used for Gaussian-restricted feature extraction. Considering that the high-dimensional process variables may have different impacts on the predicted quality variable and process variables effect the prediction accuracy, a novel attribute-relevant distributed variational autoencoder (AR-DVAE) is first proposed to effectively extract features from input variables with different correlations. In AR-DVAE, by performing correlation analysis between the input variables and the quality variable, a distributed structure with two encoder subnets is constructed. In each encoder subnet, the input attributes have the same correlation with the output quality variable. As a consequence, the proposed AR-DVAE can extract features with dual channels. Next, a supervised regressor based on the long short-term memory machine shortened to LSTM is utilized for developing soft sensing between the extracted features and the quality variable. Case studies on two actual industrial processes are carried out to confirm the high accuracy of the presented AR-DVAE integrated with the LSTM soft sensing model.