부산대학교 도서관

The detection and diagnosis of oscillation are of great importance to maintain the control performance of the process plant. Even though some algorithms based on time-frequency analysis for multiple oscillations diagnosis have been reported in the literature, their performance is severely constrained by the mode-mixing and mode-splitting problems. The presence of these issues leads not only to incorrectly detected number of oscillations, but also to incorrect identification of the oscillation sources. Motivated by the above challenge, this work proposes a novel adaptive time-frequency tool: median complementary ensemble empirical mode decomposition (MCEEMD) to construct a robust and automated framework for oscillation detection and diagnosis. In addition to detecting and diagnosing various single/multiple oscillations, the proposed method can distinguish oscillations with linear and nonlinear causes. Moreover, benefiting from the robustness of MCEEMD to mode mixing and mode splitting, the framework offers better accuracy and stability compared to state-of-the-art methods. A series of simulations and industrial cases demonstrate the effectiveness and superiority of the proposed framework.