On demodulation of time-varying oscillations in process plant. (December 2022)

• Record Type: Journal Article
• Authors: Lang, Xun; Chen, Qiming; Lu, Shan; Horch, Alexander; Zhang, Yufeng
• Is Part Of: Journal of process control. Volume 120(2022)
• Page Start: 28
• Page End: 43
• Publication Date: 2022-12
• Subjects: Amplitude and frequency demodulation -- Ensemble empirical mode decomposition -- Statistical distribution -- Teager energy operator -- Time-varying oscillation; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.10.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Due to the presence of process fluctuation, transient impacts and noise, most of the oscillations in industrial control-loops evolve with time-varying properties. If significant variation is observed under a limited monitoring window, the existing regularity-based oscillation detectors may be invalid. To tackle this problem, this paper proposes to describe the oscillations as a kind of amplitude modulated and frequency modulated (AM–FM) signals, and designs corresponding scheme to extract and detect them. More specifically, our proposed method is featured by following steps: (i) Decompose the process output into several AM–FM modes using ensemble empirical mode decomposition; (ii) Demodulate each mode with Teager energy operator for extracting the corresponding instantaneous amplitude and frequency functions; (iii) Identify the oscillatory variables by developing a new statistical measure based on the deviation of the frequency variation. The applicability of the proposed method is validated through a set of simulations and representative chemical applications. Comparative studies also demonstrate that it outperforms existing time–frequency tools and typical regularity-based detectors. Highlights: Demodulate time-varying oscillations from the process control systems. Describe oscillations as amplitude modulated and frequency modulated signals. Change the traditional global analysis of oscillations to a local one. Monitor oscillations by developing the instantaneous frequency based statistic. Method outperforms existing time–frequency tools and advanced detectors.