Overview of Process Fault Diagnosis

Abstract

This overview of process fault diagnosis concentrates on steady-state processes, continuous dynamic processes and batch processes. In steady-state processes, the classic linear model for process fault diagnosis based on the use of principal component analysis is discussed in some detail, followed by extensions of this model to nonlinear steady-state (non)Gaussian processes. These extensions include higher-order statistical models, such as based on the use of independent components, the use of principal curves and surfaces as well as neural networks as nonlinear extensions of principal component analysis. Likewise, innovations and applications of kernel methods are among other considered, including kernel principal component analysis, kernel partial least squares, kernel independent component analysis as well as multiple kernel learning variants of some of these approaches. Continuous dynamic processes are considered in terms of manifold models, adaptive methods and phase space methods, where the application of process diagnostics, such as correlation dimension and recurrence quantitative analyses, has been proposed. The multitude of recent developments in batch processing are similarly reviewed in terms of the multiway principal component model, extended to multiphase and multiblock models. These developments are considered in the broad framework outlined in Chap. 1.