Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method
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 pap...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9745576/ |
| _version_ | 1828290870610231296 |
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| author | Zhuliang Lin Min Sun Xialai Wu |
| author_facet | Zhuliang Lin Min Sun Xialai Wu |
| author_sort | Zhuliang Lin |
| collection | DOAJ |
| description | 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 <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> and penalty coefficient <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>. 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. |
| first_indexed | 2024-04-13T10:42:49Z |
| format | Article |
| id | doaj.art-a0fd1b1cbb4e43a28814de559db4c1d9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T10:42:49Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a0fd1b1cbb4e43a28814de559db4c1d92022-12-22T02:49:52ZengIEEEIEEE Access2169-35362022-01-0110361063612210.1109/ACCESS.2022.31637549745576Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition MethodZhuliang Lin0Min Sun1Xialai Wu2https://orcid.org/0000-0003-3654-5427Xingzhi College, Zhejiang Normal University, Lanxi, ChinaZhejiang Kende Mechanical and Electrical Company Ltd., Taizhou, ChinaSchool of Engineering, Huzhou University, Huzhou, ChinaIn 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 <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> and penalty coefficient <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>. 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.https://ieeexplore.ieee.org/document/9745576/Oscillation detectionmultivariate variational mode decompositionsignal decompositioncontrol performance assessment |
| spellingShingle | Zhuliang Lin Min Sun Xialai Wu Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method IEEE Access Oscillation detection multivariate variational mode decomposition signal decomposition control performance assessment |
| title | Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method |
| title_full | Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method |
| title_fullStr | Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method |
| title_full_unstemmed | Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method |
| title_short | Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method |
| title_sort | detecting and diagnosing process nonlinearity induced unit wide oscillations based on an optimized multivariate variational mode decomposition method |
| topic | Oscillation detection multivariate variational mode decomposition signal decomposition control performance assessment |
| url | https://ieeexplore.ieee.org/document/9745576/ |
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