Abstract As the deep learning technology develops, many process monitoring methods based on auto-encoder (AE) are designed for the nonlinear industrial processes. However, these methods mainly focus on process variables and ignore the quality indicator which is crucial for the final production. To extract the latent variables which represent both process information and quality information, this paper proposes a novel algorithm named information concentrated variational auto-encoder (IFCVAE). To concentrate the quality-related information, a loading matrix regularization based on mutual information is designed, so that the strongly quality-related variables tend to have larger weights in the loading matrix. In addition, to monitor processes from the quality-related and unrelated aspects, IFCVAE decomposes the original space into two subspaces that are mutually orthogonal based on variational auto-encoder (VAE). With the help of an additional regression network, the two subspaces can correspond to the quality-related and unrelated spaces. For process monitoring, two statistics are designed for the subspaces according to Kullback–Leibler divergence. Finally, the effectiveness of IFCVAE is demonstrated by a numerical case and an industrial case.

中文翻译：

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用于质量相关非线性过程监控的信息集中变分自动编码器

摘要 随着深度学习技术的发展，许多基于自动编码器（AE）的过程监控方法被设计用于非线性工业过程。然而，这些方法主要关注过程变量，而忽略了对最终生产至关重要的质量指标。为了提取代表过程信息和质量信息的潜在变量，本文提出了一种名为信息集中变分自动编码器（IFCVAE）的新算法。为了集中质量相关信息，设计了基于互信息的加载矩阵正则化，使得与质量相关的变量在加载矩阵中往往具有更大的权重。此外，从质量相关和不相关的方面监控过程，IFCVAE 基于变分自编码器 (VAE) 将原始空间分解为两个相互正交的子空间。在额外的回归网络的帮助下，两个子空间可以对应质量相关和不相关的空间。对于过程监控，根据 Kullback-Leibler 散度为子空间设计了两个统计量。最后，通过数值案例和工业案例证明了 IFCVAE 的有效性。