Autoregressive double latent variables probabilistic model for higher-order dynamic process monitoring

Abstract

The application of multivariate statistical analysis in process monitoring has emerged as a significant research topic, with a focus on consideration of data correlations. The present study investigates an anomaly detection method based on autoregressive double latent variables probabilistic (ADLVP) model for industrial dynamic processes. Specifically, the ADLVP model incorporates two distinct types of latent variables (LVs) to capture the internal relationships within the data from both quality-correlated and uncorrelated perspectives. Moreover, the model employs autoregressive modeling to characterize the double latent variables with time-dependence, enabling them to unveil more intricate higher-order autocorrelations between industrial measurements. The model parameters and the double latent variables can be iteratively determined using the expectation maximization (EM) algorithm, upon which the statistics for process monitoring are devised. Finally, the proposed method is validated in two industrial studies, and experimental results demonstrate that the ADLVP model outperforms its counterparts in dynamic processes monitoring.