Industrial Big Data Modeling and Monitoring Framework for Plant-Wide Processes

Abstract

This article proposes a distributed parallel modeling and monitoring framework for plant-wide processes with big data. The “distributed” contains two layers of meaning. One is the spatially distributed modeling and hierarchical monitoring for the plant-wide process with multiple operating units. The other represents the distributed parallel modeling for big process data with various features. Under the framework, the distributed parallel mixture probabilistic latent variable model is proposed based on the stochastic variational inference algorithm and the parameter server architecture to cope with the big process data. Then, the model is utilized to develop the plant-wide hierarchical and distributed process monitoring algorithms, where the multilevel monitoring indexes and fault contribution indexes are established based on the Bayesian fusion algorithm for process fault detection and diagnosis. The performance comparison and visualization for the industrial plant-wide process case has demonstrated the reliability and superiority of the proposed algorithm and framework.