Detecting Multiple Plant-wide Oscillations in Process Control Systems Based on Multivariate Intrinsic Chirp Component Decomposition

Chen, Qiming; Lang, Xun; Pan, Yi; Shi, Yao; Xie, Lei; Su, Hongye

doi:10.1109/safeprocess52771.2021.9693592

Cited by 1 publication

(1 citation statement)

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“…In order to illustrate the influence of mode number K, Figure 5 and 6 display the decomposition results of MVMD with K = 2 and K = 4, respectively. It can be seen that MVMD shows underdecomposition and over-decomposition issue [36], [37] in Figure 5 and 6, respectively.Therefore, it implies that determining the appropriate mode number is necessary for MVMD to decompose signals correctly. These comparative experimental results show that the proposed SSA-MVMD effectively improves the decomposition performance of the original MVMD by optimizing the parameter pairs.…”

Section: A Numerical Examplementioning

confidence: 98%

Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method

Lin

Sun²,

2022

IEEE Access

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In process control system, nonlinearity-induced unit-wide oscillations are a common fault, which degrades the control performance and threaten the stability. It is important to detect and diagnose the nonlinearity-induced unit-wide oscillations to improve the process control performance. In this paper, a novel method, termed as SSA-MVMD, is proposed by combining the sparrow search algorithm (SSA) and multivariate variational mode decomposition (MVMD) to detect and diagnose the nonlinearity-induced unit-wide oscillations. MVMD is an advanced signal decomposition and time-frequency method. However, its performance is affected by the mode number K and penalty coefficient α. SSA is adopted to optimize the parameters of MVMD. Then, a novel SSA-MVMD-based detector is presented to detect and diagnose the nonlinearity-induced unit-wide oscillations. The proposed method is model-free and data-driven thus requiring no prior knowledge about the process dynamics. Compared with the latest related works, the proposed method can better decompose the multivariate nonstationary signals and adaptively analyze the unit-wide oscillations. In the end, the effectiveness and advantages are demonstrated by simulations as well as industrial cases.

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Section: A Numerical Examplementioning

confidence: 98%