Nonlinear Process Monitoring: Part 2

Abstract

This chapter introduces a linear subspace and Bayesian inference-based monitoring method for nonlinear processes. Through the linear subspace method, the original nonlinear space can be approximated by several linear subspaces, based on which different monitoring sub-models have been developed. A subspace contribution index has been defined for variable selection in each subspace. Monitoring results are first generated in each subspace, and then transferred to fault probabilities by the Bayesian inference strategy. To make the final monitoring decision, subspace monitoring results are combined together with their fault probabilities. In addition, a corresponding fault diagnosis method has also been developed. To demonstrate the computationally efficiency of the new method, detailed comparisons of the algorithm complexity for different methods are provided in this chapter.