A Novel Multimanifold Joint Projections Model for Multimode Process Monitoring

Abstract

Complex industrial processes are commonly characterized with multiple operation modes. The existing manifold learning-based process monitoring methods describe each mode individually without capturing the connections among different modes, which may deteriorate the monitoring capability. This article proposes a novel dimensionality reduction model referred as to multimanifold joint projections to monitor the multimode processes, where the intramode and the intermode adjacency matrices are constructed to reflect the underlying features within each mode and among different modes, respectively. The neighboring and nonneighboring structures of data within each mode are captured by the distance and angle information of pairwise points to reveal the intrinsic structure of the original data, thus offering a more faithful representation of multimodal data and further enhancing monitoring performance. During online monitoring, a point to manifold distance criterion is proposed to determine the running-on mode of new samples. Two case studies demonstrated the superior performance of the proposed approach in multimode process monitoring.