Detection and time–frequency analysis of multiple plant-wide oscillations using adaptive multivariate intrinsic chirp component decomposition

• Published in: Control engineering practice Vol. 141; p. 105715
• Main Authors: Chen, Qiming, Wen, Qingsong, Wu, Xialai, Lang, Xun, Shi, Yao, Xie, Lei, Su, Hongye
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Adaptive multivariate intrinsic chirp component decomposition; Control performance assessment; Multivariate signal decomposition; Multivariate time–frequency analysis; Plant-wide oscillation detection; Adaptive multivariate intrinsic chirp component decomposition; Multivariate signal decomposition; Multivariate time–frequency analysis; Plant-wide oscillation detection; Control performance assessment
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/j.conengprac.2023.105715

Analyzing plant-wide oscillations is a challenging task owing to the presence of noise, nonstationarity, and multiple modes in a process control system. Multivariate intrinsic chirp component decomposition (MICCD) is a novel powerful tool for multivariate signal processing. Nevertheless, MICCD requires users to provide component number in advance, which restricts its adaptability. This study proposes an adaptive MICCD (AMICCD) that can adaptively determine the component number by utilizing the permutation entropy of instantaneous frequency. An AMICCD-based time–frequency analysis framework is presented to detect and characterize the multiple plant-wide oscillations. Compared to the latest methods, such as multivariate empirical mode decomposition and multivariate intrinsic time-scale decomposition, the proposed method can process not only single/multiple plant-wide oscillations, but also time-invariant/time-varying plant-wide oscillations. In particular, the proposed method can directly provide the time–frequency curves of multiple plant-wide oscillations, which have not been achieved by the state-of-the-art techniques. Finally, the effectiveness and advantages of the proposed approach are demonstrated on a wide variety of simulations and industrial cases. •An AMICCD algorithm is proposed.•Compared with the SOTA, AMICCD shows the best decomposition performance.•An AMICCD-based detector is developed for detecting plant-wide oscillations.