MNCMD-based causality analysis of plant-wide oscillations for industrial process control system
Published in Chinese Automation Congress (CAC) 2020, 2020
Recommended citation: Qiming Chen, Xiaozhou Xu, Yao Shi, Xun Lang, Lei Xie, Hongye Su. Chinese Automation Congress (CAC). (2020). https://ieeexplore.ieee.org/abstract/document/9327085
In this paper, a data-driven model combining multivariate nonlinear chirp mode decomposition (MNCMD) with multivariate Granger causality (MGC) is proposed to analyze root causes for multiple plant-wide oscillations in process control system. Firstly, an MNCMD-based detector is developed to capture the multiple plant-wide oscillations, where oscillating variables caused by different sources are automatically clustered into various groups. Then, MGC is applied to each group to obtain the root causes of multiple plant-wide oscillations. Compared with state-of-the-art detection methods, the proposed approach shows better performance in the following aspects: (i) ability to extract both single/multiple plant-wide oscillations; (ii) capability to process both time-invariant/time-varying oscillations and provide accurate time-frequency information. The effectiveness and advantages of the proposed approach are demonstrated with the help of both simulation and industrial case studies.