Dual adversarial learning-based virtual sample generation method for data expansion of soft senors

Abstract

Many key quality variables are difficult to measure in complex industrial processes for various reasons, such as working conditions or economic costs, leading to inefficient production monitoring. In recent years, soft sensors with outstanding performance in variable estimation have been widely used. However, quality samples collected from industrial sites are often limited, which results in incomplete datasets that cannot meet the training requirements of soft sensors and poor performance in model learning and prediction. In this paper, a new virtual sample generation method DA-GAN based on generative adversarial network (GAN) is proposed to provide extra training samples for soft sensors. Adversarial net-and adversarial sample-based dual adversarial learning is implemented to reduce the adversarial noise in the discriminator gradient, which can improve the convergence speed and learning stability of the generator and obtain virtual samples with higher similarity to the real data. Furthermore, a sample screening method based on asymmetric acceptable domain range expansion is introduced to choose high-quality virtual samples. Experimental results of two industrial case studies show that the virtual samples provided by DA-GAN are closer to real samples than several other widely used generation methods. The performance of the prediction model trained with the dataset added by the virtual samples yielded from DA-GAN can be better improved.