Spatio‐temporal adaptive soft sensor for nonlinear time‐varying and variable drifting processes based on moving window LWPLS and time difference model

Abstract

AbstractIndustrial plants often undergo different kinds of changes like variable drifts and time‐variant problems, which may cause the degradation of soft sensors. In this paper, a spatio‐temporal adaptive soft sensor modeling framework, which is based on the moving window and just‐in‐time learning (JITL) techniques, is proposed for nonlinear time‐varying processes. The JITL (locally weighted partial least squares) can adapt the model by spatial weighting technique, and the moving window technique can adapt the soft sensor model to the new process state. Furthermore, time difference (TD) model is utilized to handle the process change of variable drifts. Case studies are carried out on a numerical example and two industrial processes. The results show the effectiveness and flexibility of the proposed soft sensor framework. © 2015 Curtin University of Technology and John Wiley & Sons, Ltd.