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，针对非线性工业过程开发了许多基于深度学习的故障检测方法。但是，他们中的大多数都没有考虑与质量相关的故障。为了提取可以表示分离的质量相关和无关信息的潜在变量，本文提出了一种新的深度 VIB-VAE 算法，该算法结合了变分自编码器 (VAE) 模型和深度变分信息瓶颈 (VIB)。Deep VIB 通过最大化潜在变量和过程质量之间的互信息同时最小化潜在变量和观察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。为了监测和区分质量相关故障和质量无关故障，由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的性能。最后，所提出的深度 VIB-VAE 算法的有效性分别通过数值案例和真实的热轧带钢工艺案例来证明。