Time-Varying Process Monitoring

Abstract

In this chapter, an adaptive local model-based monitoring approach is introduced for online monitoring of nonlinear time-varying processes with non-Gaussian information. To solve the time-varying problem, a just-in-time-learning (JITL) strategy is introduced. The local least squares support vector regression (LSSVR) model is built upon the relevant dataset for prediction. To satisfy the online modeling demand, the real-time problem is considered. Then, a two-step independent component analysis-principal component analysis (ICA-PCA) information extraction strategy is introduced to analyze residuals between the real output and the predicted one. A simulation case study shows that the new method gives better performance compared to conventional methods.