GDVAE: A Gated Recurrent Deep Shrinkage Variational Autoencoder for Dynamic Nonlinear Process Monitoring

Abstract The safe operation of process industries requires accurate anomaly monitoring models that can effectively model intricate time-varying and nonlinear variable dynamics to minimize false alarms. This paper introduces the Gated Recurrent Deep Shrinkage Variational Autoencoder (GDVAE), a novel framework for dynamic nonlinear process monitoring. The method first utilizes a recursive Variational Autoencoder (VAE) to capture temporal dependencies and characterize the data distribution from normal operations, enabling precise online data reconstruction. Following this, a deep residual shrinkage network (DRSN) is employed to assign unique weights to process variables based on their fault relevance within a given time window. Finally, a novel monitoring statistic is designed by synergizing the reconstruction error and the assigned fault feature weights for real-time process monitoring. We validated our method on the benchmark Tennessee Eastman (TE) process. Comparative analyses demonstrate that the proposed GDVAE model achieves significant improvements in detection accuracy and reliability over conventional Dynamic Principal Component Analysis (DPCA), Kernel Principal Component Analysis (KPCA), and standard VAE methods, offering a more robust solution for dynamic nonlinear process monitoring.