Statistical modeling and online fault detection for multiphase batch processes with analysis of between-phase relative changes

Abstract

For multiphase batch processes, the specific changes of process characteristics between two neighboring phases are a critical problem for batch process monitoring, which, however, have not been fully addressed. This article proposes a statistical modeling and online fault detection strategy with the analysis of between-phase relative changes for multiphase batch processes. A two-step subspace decomposition procedure is designed to explore the between-phase relationship by relating each phase with its neighboring phases. Different subspaces are decomposed from the original phase space to reveal different changes from one phase to another, including increased part, decreased part and unchanged part. They are deemed to impose different influences on monitoring results in systematic and residual monitoring subspaces, respectively. The process dynamics from one phase to another are then comprehensively captured in different subspaces. By modeling and monitoring different types of between-phase relative variations, the proposed algorithm can provide enhanced process understanding about between-phase relationship and also offer reliable fault detection performance. The proposed algorithm is illustrated with a typical multiphase batch process, injection molding.