Multiscale process monitoring with singular spectrum analysis

Abstract Multivariate statistical process control (MSPC) approaches are now widely used for performance monitoring, fault detection and diagnosis in chemical processes. Conventional MSPC approaches are based on latent variable projection methods such as principal component analysis and partial least squares. These methods are suitable for handling linearly correlated data sets, with minimal autocorrelation in the variables. Industrial plant data invariably violate these conditions, and several extensions to conventional MSPC methodologies have been proposed to account for these limitations. In this paper a multiscale methodology based on the use of singular spectrum analysis is proposed and its advantages are demonstrated via illustrative case studies.