Process monitoring plays an essential role in safe and profitable operations. Various data-driven fault detection models have been suggested, but they cannot perform properly when the training data are insufficient or the information to construct the manifold is confined to a specific region. In this study, a process monitoring framework integrated with data augmentation is proposed to supplement rare but informative samples for the boundary regions of the normal state. To generate data for augmentation, a variational autoencoder was employed to exploit its advantage of stable convergence. For the construction of the process monitoring system, an autoencoder that can extract useful features in an unsupervised manner was used. To illustrate the efficacy of the proposed method, a case study for the Tennessee Eastman process was applied. The results show that the proposed method can improve the monitoring performance compared to the autoencoder without data augmentation in terms of fault detection accuracy and delay, particularly within the feature space.

过程监控在安全和有利可图的操作中起着至关重要的作用。已经提出了各种数据驱动的故障检测模型，但是当训练数据不足或构建流形的信息仅限于特定区域时，它们无法正常执行。在这项研究中，提出了一种与数据增强相结合的过程监控框架，以补充正常状态边界区域的稀有但信息丰富的样本。为了生成用于增强的数据，采用了变分自编码器来利用其稳定收敛的优势。为了构建过程监控系统，使用了可以以无监督方式提取有用特征的自动编码器。为了说明所提出方法的有效性，应用了田纳西伊士曼过程的案例研究。