A Novel Distributed CVRAE-Based Spatio-Temporal Process Monitoring Method With Its Application

Abstract

Due to the interconnected characteristics between subsystems and the strong correlation within subsystems, the monitoring of plant-wide processes has become a challenging problem, especially for tandem plant-wide processes that exist in various industrial fields, such as petrochemicals, metallurgy and sewage treatment. In this paper, a novel spatio-temporal monitoring method is proposed for the hot strip mill process, a typical tandem industrial process. Firstly, the plant-wide process is divided into different sub-blocks based on the tandem structure. Then, a distributed conditional variational recurrent autoencoder (CVRAE)-based process monitoring method is proposed to build the local latent variable model of each subsystem using relevant dynamic features extracted from the previous subsystem. The latent distributions and reconstructed errors are used to design local monitoring statistics for local process monitoring. A global monitoring statistic is established by deep support vector data description (SVDD) to monitor the whole process. Finally, the effectiveness and superiority of the proposed method are demonstrated by a hot strip mill (HSM) process case, which shows better monitoring performance compared to the existing methods.