Abstract: Modern industrial processes are typically characterized by large-scale and intricate internal relationships. Therefore, the distributed modeling process monitoring method is effective. A novel distributed monitoring scheme utilizing the Kantorovich distance-multiblock variational autoencoder (KD-MBVAE) is introduced. Firstly, given the high consistency of relevant variables within each sub-block during the change process, the variables exhibiting analogous statistical features are grouped into identical segments according to the optimal quality transfer theory. Subsequently, the variational autoencoder (VAE) model was separately established, and corresponding T² statistics were calculated. To improve fault sensitivity further, a novel statistic, derived from Kantorovich distance, is introduced by analyzing model residuals from the perspective of probability distribution. The thresholds of both statistics were determined by kernel density estimation. Finally, monitoring results for both types of statistics within all blocks are amalgamated using Bayesian inference. Additionally, a novel approach for fault diagnosis is introduced. The feasibility and efficiency of the introduced scheme are verified through two cases.

Key words: Chemical processes, Safety, Kantorovich distance, Neural networks, Process monitoring, Bayesian inference

摘要： Modern industrial processes are typically characterized by large-scale and intricate internal relationships. Therefore, the distributed modeling process monitoring method is effective. A novel distributed monitoring scheme utilizing the Kantorovich distance-multiblock variational autoencoder (KD-MBVAE) is introduced. Firstly, given the high consistency of relevant variables within each sub-block during the change process, the variables exhibiting analogous statistical features are grouped into identical segments according to the optimal quality transfer theory. Subsequently, the variational autoencoder (VAE) model was separately established, and corresponding T² statistics were calculated. To improve fault sensitivity further, a novel statistic, derived from Kantorovich distance, is introduced by analyzing model residuals from the perspective of probability distribution. The thresholds of both statistics were determined by kernel density estimation. Finally, monitoring results for both types of statistics within all blocks are amalgamated using Bayesian inference. Additionally, a novel approach for fault diagnosis is introduced. The feasibility and efficiency of the introduced scheme are verified through two cases.

关键词: Chemical processes, Safety, Kantorovich distance, Neural networks, Process monitoring, Bayesian inference