Distributed temporal-spatial neighbourhood enhanced variational autoencoder for multiunit industrial plant-wide process monitoring

Process monitoring plays an essential role in ensuring the safe and efficient operation of multi-unit plantwide industrial processes. However, complicated nonlinear dynamics of these processes pose enormous challenges to the plantwide process monitoring approaches. In this paper, a novel distributed temporal-spatial neighbourhood enhanced variational autoencoder (DTS-VAE) monitoring scheme is proposed. The scheme selects the temporal and spatial neighbourhood sets of a current sample from a local unit. Time-series correlation is utilized to construct temporal representative samples to obtain process dynamic characteristics. Likewise, spatial similarity is employed to construct spatial representative samples to obtain spatial patterns in the data. Then the reconstructed and current samples serve as the inputs for a variational autoencoder to extract features from the current sample and its neighbourhoods. Subsequently, a distributed monitor that considers the temporal-spatial characteristics is established for each unit. Finally, a comprehensive evaluation index is developed using Bayesian fusion strategy to improve monitoring performance. The proposed DTS-VAE monitoring scheme was effectively verified on the Tennessee Eastman benchmark process and a wastewater treatment plant.