Key result
An improved variational mode decomposition method successfully detected nonlinearity-induced oscillations and distinguished multiple oscillations in process control loops.
Why the study?
The effectiveness of variational mode decomposition depends heavily on parameters including mode number, initial center frequency, and penalty coefficient when detecting nonlinearity-induced oscillations.
An improved VMD method using PRSA and SPE effectively detects nonlinearity-induced oscillations in process control loops.
May enhance industrial oscillation detection; leaves open validation for cardiovascular monitoring applications.
A novel detector based on improved variational mode decomposition (VMD) is proposed to detect the nonlinearity-induced oscillations. Despite its high adaptivity and frequency resolution, the effectiveness of VMD highly depends on parameters, including mode numberK, initial center frequencyω ᵢₙᵢₜ, and the penalty coefficientα. To tackle this problem, an improved VMD is proposed, which involves: 1) the spectrum of phase-rectified signal averaging (PRSA) to determine optimalK, ω ᵢₙᵢₜand 2) the summation of permutation entropy (SPE) to optimizeα, respectively. The presence of nonlinearity can be monitored by investigating the relationships among different frequencies of the process variable (PV) in the control loops. In addition, the oscillation detector based on the improved VMD is capable of distinguishing multiple oscillations, even when both nonlinear and linear oscillations from different sources occur. The proposed method is completely adaptive and data driven, which acts withouta prioriknowledge. The validity of the raised approach is verified by a set of simulations as well as industrial applications.
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Chen et al. (2019) studied Nonlinearity-induced oscillations in process control loops. Improved Variational Mode Decomposition (VMD) vs. Standard VMD, EMD, LMD, and MEMD was evaluated on Detection of nonlinearity-induced oscillations. An improved variational mode decomposition method successfully detected nonlinearity-induced oscillations and distinguished multiple oscillations in process control loops.
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