Detecting Nonlinear Oscillations in Process Control Loop Based on an Improved VMD

Chen, Qiming; Lang, Xun; Xie, Lei; Su, Hongye

doi:10.1109/access.2019.2925861

Cited by 30 publications

(22 citation statements)

References 35 publications

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“…In the end, the SISO experiments and various industrial cases demonstrate the utility and advantages of the proposed oscillation detection and characterization framework. Compared with the the existing detection techniques based on EMD [13] and VMD [22], the proposed method is more accurate and reliable. Therefore, it can be concluded that this study has great potential values for control performance monitoring.…”

Section: Discussionmentioning

confidence: 97%

“…The detection and characterization results listed in Table 2 are well consistent with the prior knowledge, which demonstrates the effectiveness of the proposed method. In order to highlight the advantages of the proposed [13] and VMD-based [22] methods are used to detect the oscillations contained in these data. The decomposition plots for the three misclassified cases using EMD (for pulp and papers, metals), or VMD (for mining) are provided in Figure 20, 21, and 22, respectively.…”

Section: Industrial Casesmentioning

confidence: 99%

“…Although VMD shows good performance in anti-mode-mixing and robustness, it is limited to decomposing narrow-band signals. It only could provide the center frequencies (scalar), which indicates a lack of time-frequency representation [22]. Recently, Chen et al [23] proposed a nonlinear chirp mode decomposition (NCMD) algorithm, which is the latest development in the field of signal decomposition.…”

Section: Introductionmentioning

confidence: 99%

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Detecting and Characterizing Multiple Oscillations in Process Control Loop Based on PSO-NCMD

et al. 2020

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Oscillations are a common abnormal phenomenon in the process control system. They can degrade control performance or even cause plant shutdown. It is crucial to accurately detect and characterize the process oscillations. In this paper, a novel oscillation detector is proposed by combining the particle swarm optimization (PSO) and nonlinear chirp mode decomposition (NCMD). Because the performance of NCMD relies on the selection of mode number Q and bandwidth parameter α, PSO is utilized to search the optimal parameter pairs. Then, the multiple oscillations contained in the process variables (PV) can be extracted by NCMD with the optimal parameters. The normalized correlation index and sparseness index are used to discarding the spurious modes and quantifying the degree of oscillations, respectively. After detecting, by utilizing the time-frequency information provided by the oscillating modes, multiple oscillation types can be accurately characterized. Comparisons are provided to show the advantages of the proposed method. The effectiveness and utility are validated by simulations as well as various industrial cases. INDEX TERMS Oscillation detection, nonlinear chirp mode decomposition, particle swarm optimization, control performance assessment.

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Section: Discussionmentioning

confidence: 97%

Section: Industrial Casesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting and Characterizing Multiple Oscillations in Process Control Loop Based on PSO-NCMD

et al. 2020

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“…Oscillations are one of the most common problems in process control systems [1]. Oscillation behavior will seriously affect the control system performance, such as reducing the product quality and uniformity, increasing the energy consumption, wasting the raw materials and even causing the plant down [2]. It is reported that over 30% control loops are oscillating because of the presence of nonlinearity problems [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Detecting and Analyzing Nonlinearity-Caused Oscillations in Process Control Systems Using an Improved VNCMD

et al. 2021

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Nonlinearity-caused oscillations are a frequent issue in process control systems. Its incidence degrades the product quality, stability and safety of the plant. Therefore, it is important to detect and analyze the nonlinearity-caused oscillations to maintain the control performance. In this study, we propose a novel oscillation detection and analyze method based on an improved variational nonlinear chirp mode decomposition (VNCMD) algorithm. Specifically, the original VNCMD needs to manually set the mode number in advance, which is a challenging task in practice. To tackle this problem, an improved VNCMD (IVNCMD) is proposed by utilizing the approximate entropy of instantaneous frequency. Then a novel IVNCMD-based detector is developed to detect and analyze the nonlinearity-induced oscillations by revealing the harmonic content of process variable. Besides detecting the nonlinearity problem, the IVNCMD-based method can contribute in locating the root cause for nonlinearity-caused 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 methods, the proposed method is able to process nonstationary oscillations and providing corresponding time-frequency information. The effectiveness and advantages are demonstrated through simulations as well as industrial applications.

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“…These methods firstly decompose the multi-component acceleration signals into single-component intrinsic mode functions (IMFs) using the mode decomposition method, and then, identify the instantaneous frequency of the IMFs using time-frequency analysis method. Among all the mode decomposition methods, variational mode decomposition (VMD) [38] is gradually well-known because it can overcome the shortcomings of modal aliasing [39], [40]; Among all the time-frequency analysis method, synchrosqueezed wavelet transform (SWT) [41] is widely used because it can obtain time-frequency distribution curves with high frequency accuracy [42], [43].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Identifying Railway Track Rail’s Modal Frequency From Wheel-Rail Excitation and Its Application in High-Speed Railway Monitoring

Gao

Xin

et al. 2019

IEEE Access

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High-speed railway track defects such as rail corrugation, wheel polygonal wear, and rail fastener clip failure are closely related to the modal frequency of the track structure. As the operation time increases, the modal parameters of the track structure will change, and it is necessary to know what they are to diagnose the vibration characteristics. Present methods of modal analysis are limited by measuring points and excitations and cannot be used to extract the operational parameters of the railway track. This paper proposes a novel approach using wheel-rail excitations to identify the rail modal frequency from vibration monitoring data. The approach decomposes the rail acceleration after the wheel-rail excitation passes into intrinsic mode functions and extracts their instantaneous frequencies. The modal frequencies of the rail can be identified from the instantaneous frequencies. To demonstrate the feasibility of the approach, the results using the proposed approach are compared with the modal frequencies of the rail identified using the eigensystem realization algorithm. Then the approach is applied to study the influence of the number of wheel-rail excitations and the number of monitoring points on the identification results in high-speed railway ballastless track rail. The paper concludes that the proposed approach can provide a reliable solution for the identification of the operational modal frequency of track rails based on the monitoring data. INDEX TERMS High-speed railway track, operational modal frequency, synchrosqueezed wavelet transform, variational mode decomposition.

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Detecting Nonlinear Oscillations in Process Control Loop Based on an Improved VMD

Cited by 30 publications

References 35 publications

Detecting and Characterizing Multiple Oscillations in Process Control Loop Based on PSO-NCMD

Detecting and Characterizing Multiple Oscillations in Process Control Loop Based on PSO-NCMD

Detecting and Analyzing Nonlinearity-Caused Oscillations in Process Control Systems Using an Improved VNCMD

A Novel Approach of Identifying Railway Track Rail’s Modal Frequency From Wheel-Rail Excitation and Its Application in High-Speed Railway Monitoring

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