Spatial–Temporal Deviation Analysis for Multivariate Statistical Process Monitoring

Abstract

Given that an effective process monitoring implementation should take both the spatial and temporal variations into account, a novel online process monitoring scheme based on a newly formulated algorithm titled as spatial–temporal deviation analysis (STDA) is proposed. Different from the mainstream process monitoring methods that focus on characterizing the spatial and/or temporal variation in the historical normal samples, the proposed STDA algorithm is designed to adaptively and timely train a pair of projecting vectors to uncover potential deviation in the spatial–temporal variation of online monitored samples, so as to guarantee consistently enhanced monitoring performance. Instead of utilizing a fixed projecting framework trained offline, the STDA algorithm is repeatedly executed once a newly measured sample become available for online monitoring. Therefore, the proposed STDA-based method could consistently ensure its effectiveness for online fault detection, because a projecting framework targeted to revealing deviation in spatial–temporal variation is dynamically determined for different online monitoring samples in a timely manner. Finally, the salient monitoring performance achieved by the proposed STDA-based approach is evaluated through comparisons with other counterparts.