Diagnosis of plant-wide oscillations by combining multivariate empirical mode decomposition and delay vector variance

A combination of multivariate empirical mode decomposition (MEMD) and the delay vector variance (DVV) method is explored for the diagnosis of plant-wide oscillations. The proposed algorithm improves upon the already presented method based solely on the DVV approach. The proposed modification makes it possible for the DVV method to work not only for the non-stationary data but also the case where multiple oscillations sources are present in the control loop. The focus of this work has been to distinguish between linear and non-linear causes of oscillations and isolating the source of non-linearity. A noise assisted multivariate empirical mode decomposition is used to sift out different oscillating components in the plant wide data and the nature of the oscillation is then ascertained using the already presented DVV approach. In the case of oscillations due to non-linear causes the method can isolate the source using the extent of non-linearity. The method is tested on simulated and industrial case studies and the results are compared with existing methods based on higher order statistics [8] and surrogate based methods [27]. (C) 2019 Elsevier Ltd. All rights reserved.