Attention-Driven Supervised Dynamic Latent Variable Models for Monitoring Dynamic Industrial Process

The accurate measurement and monitoring of quality variables are crucial for optimizing productivity and efficiency in industrial processes. However, many existing data-driven methods fail to account for the dynamic nature of industrial data and the nonuniform distributions that arise due to fluctuating operational conditions. To address these challenges, attention-based dynamic latent variable models are proposed. This approach comprehensively considers time-variant relationships among latent variables using attention mechanisms for both samples and variables. An interactive iterative algorithm is introduced to compute model parameters efficiently. Experimental validation, conducted through both numerical simulations and real-world applications in an industrial ethylene oxychlorination process, demonstrates the effectiveness of the proposed approach in enhancing process modeling and prediction performance. Comparison results show that the fault detection rate (FDR) of the proposed attention-based methods is significantly higher than that of traditional approaches. The proposed bi-attention partial least squares (PLSs) achieves the highest FDR rate of 0.94 based on the squared prediction error (SPE) statistic in the application of industrial application.