Tang Peng, Peng Kaixiang, Dong Jie
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
ISA Trans. 2021 Aug;114:444-454. doi: 10.1016/j.isatra.2021.01.002. Epub 2021 Jan 11.
Deep learning has gotten much attention in industrial field, many fault detection methods based on deep learning have been developed for nonlinear industrial processes. However, most of them do not take the quality-related faults into account. In order to extract the latent variables which can represent the separated quality-related and unrelated information, this paper proposes a novel deep VIB-VAE algorithm, which combines variational autoencoder (VAE) model and deep variational information bottleneck (VIB). Deep VIB extracts quality-related latent variables by maximizing mutual information between latent variables and process quality while minimizing mutual information between latent variables and observation. VAE is used to learn the quality-unrelated part with above quality-related latent variables as auxiliary information. To monitor and distinguish quality-related and quality-unrelated faults, two monitoring statistics are designed by the two-part latent variables. The reconstruction error by VAE is introduced to improve the performance of fault detection. Finally, the effectiveness of the proposed deep VIB-VAE algorithm is demonstrated by a numerical case and a real hot strip mill process case, respectively.
深度学习在工业领域备受关注，许多基于深度学习的故障检测方法已被开发用于非线性工业过程。然而，其中大多数方法并未考虑与质量相关的故障。为了提取能够表示分离的质量相关和无关信息的潜在变量，本文提出了一种新颖的深度VIB-VAE算法，该算法将变分自编码器（VAE）模型与深度变分信息瓶颈（VIB）相结合。深度VIB通过最大化潜在变量与过程质量之间的互信息，同时最小化潜在变量与观测值之间的互信息来提取与质量相关的潜在变量。VAE用于以上述与质量相关的潜在变量作为辅助信息来学习与质量无关的部分。为了监测和区分与质量相关和与质量无关的故障，利用两部分潜在变量设计了两个监测统计量。引入VAE的重构误差以提高故障检测性能。最后，分别通过一个数值案例和一个实际热轧带钢过程案例验证了所提出的深度VIB-VAE算法的有效性。
