Novel Virtual Sample Generation Using Target-Relevant Autoencoder for Small Data-Based Soft Sensor

Abstract

In the complex industrial processes, the process data have the characteristics of imbalance and are incomplete due to the difficult-to-measure key variables, leading to the performance degradation of soft sensors. In order to deal with this problem, a novel noise injection virtual sample generation approach based on target-relevant autoencoder (NITAE-VSG) is proposed in this article. Different from the conventional VSG methods, the presented NITAE-VSG model can generate useful input and output virtual samples directly. To increase the diversity of virtual samples, the Gaussian noise (G_noise) is added into the features extracted from the target-relevant autoencoder (TAE) hidden layer. The generated virtual samples are combined with the original small samples to enhance the soft sensor model performance. To verify the reliable feasibility and superior effectiveness of the presented NITAE-VSG, two actual industrial cases: an industrial process of purified terephthalic acid shortened as PTA and a production system of ethylene are carried out. The results of simulations demonstrate that the presented NITAE-VSG can generate superior virtual samples to some other virtual sample generation (VSG) methods and can effectively improve the soft sensor modeling accuracy.