NLPCA as a diagnostic tool for control valve stiction

A significant number of control loops in process plants perform poorly due to control valve stiction. Developing a method to detect valve stiction in the early phase is imperative to avoid major disruptions to the plant operations. Nonlinear principal component analysis (NLPCA), widely known for its capability in unravelling nonlinear correlations in process data, is extended in this paper to diagnose control valve stiction problems. The present work is based on distinguishing the difference between the shapes of the signals caused by stiction and other sources, and utilizes the operating data of controlled variable-controller output (pv–op). The structure of pv–op data used in this work is of sufficiently low dimension such that the NLPCA’s output allows the usage of simple mathematical tests in quantifying the nonlinear behavior of the loop. It is shown that if the underlying structure of pv–op data is linear, the NLPCA output generally approximates to a straight line with a regression coefficient (R²) greater than 0.8, otherwise there is a possibility of presence of nonlinearity or non-Gaussianity. The presence of stiction is then detected via a new and simple NLPCA curvature index, I_NC. Results from simulated and real industrial case studies show that NLPCA is a very promising tool for detecting valve stiction.