Detection and diagnosis of oscillations in process control by fast adaptive chirp mode decomposition

Abstract

Even though several algorithms have been proposed in the literature for oscillation detection and diagnosis, they can work reliably only for a specific type of oscillation and there is a lack of a common framework that accommodates the detection and diagnosis for various types of oscillations. To tackle this problem, an FACMD-based (fast adaptive chirp mode decomposition) detection and diagnosis framework is established in this study. It consists of two common oscillation detection indices and a novel strategy for diagnosing nonlinear and linear oscillations. Apart from detecting and diagnosing various single/multiple oscillations in single-input single-output (SISO) loop, FACMD can also distinguish the combination of linear or nonlinear oscillations and contribute to the root cause analysis for plant-wide oscillations. Finally, a series of simulations and industrial cases are used for testing. Compared with the existing work, the proposed methodology has better detection and diagnosis accuracy and a higher level of automation, especially in processing complex multiple oscillations.