Data-driven soft sensors have been widely used to facilitate real-time estimations of difficult-to-measure variables. However, sufficient high-quality data are often difficult to obtain due to high cost or low sampling rate. Thus, a soft sensor method based on data enhancement and selective ensemble (DESE) is proposed. First, a generative model is proposed for generating virtual labeled samples by combining supervised variational autoencoder (SVAE) and Wasserstein GAN with gradient penalty (WGAN-gp), which is referred to as SV-WGANgp. Then, SV-WGANgp is trained on various resampled training subsets to generate different sets of virtual samples. Next, diverse enhanced base models are constructed with the extended training sets. Subsequently, a multi-objective optimization (MOO) is utilized to achieve ensemble pruning. Finally, the final prediction is obtained by fusing the selected models. The application results on an industrial chlortetracycline fermentation process and a simulated penicillin fermentation process verify the effectiveness and superiority of the proposed methods.

数据驱动的软传感器已被广泛用于促进难以测量变量的实时估计。然而，由于成本高或采样率低，往往难以获得足够的高质量数据。因此，提出了一种基于数据增强和选择性集成（DESE）的软测量方法。首先，提出了一种生成模型，用于通过结合监督变分自动编码器（SVAE）和具有梯度惩罚的 Wasserstein GAN（WGAN-gp）来生成虚拟标记样本，称为 SV-WGANgp。然后，SV-WGANgp 在各种重采样训练子集上进行训练，以生成不同的虚拟样本集。接下来，使用扩展的训练集构建各种增强的基础模型。随后，利用多目标优化（MOO）来实现集成修剪。最后，通过融合所选模型获得最终预测。在工业金霉素发酵工艺和模拟青霉素发酵工艺上的应用结果验证了所提方法的有效性和优越性。