A parameter adaptive EEFO VMD method to mitigate noise and trend interference of blast vibration signals
Blasting vibration signals are often contaminated by trend terms and noise, stemming from environments, and instrument errors. This contamination hinders subsequent signal processing and analysis. To obtain a pure blasting vibration signal, a parameter-adaptive variational mode decomposition (VMD) m...
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| Published in | Scientific reports Vol. 15; no. 1; pp. 10035 - 27 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
23.03.2025
Nature Publishing Group Nature Portfolio |
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| Abstract | Blasting vibration signals are often contaminated by trend terms and noise, stemming from environments, and instrument errors. This contamination hinders subsequent signal processing and analysis. To obtain a pure blasting vibration signal, a parameter-adaptive variational mode decomposition (VMD) method based on the improved electric eel foraging optimization (EEFO) algorithm is proposed for preprocessing the blasting vibration signal. This method removes high-frequency noise and low-frequency trend components from the signal. Combining the abrupt and related characteristics of blasting vibration signals, a weighted multi-scale permutation entropy is constructed as the fitness function for parameter optimization. The EEFO algorithm, with strong global and local search capabilities, is employed to optimize the VMD decomposition parameters. This approach adaptively determines the optimal combination of decomposition modes
K
and the secondary penalty factor
α
. The analysis results of simulated vibration signals with different interference components and actual measured blasting vibration signals using this method show that, compared to traditional VMD, empirical wavelet transform, and empirical mode decomposition methods, EEFO-VMD has superior adaptability and anti-aliasing capabilities. Even in complex interference components, it can adaptively determine the optimal decomposition parameter combination, effectively removing interference components from the vibration signals. This method is suitable for preprocessing blasting vibration signals. |
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| AbstractList | Blasting vibration signals are often contaminated by trend terms and noise, stemming from environments, and instrument errors. This contamination hinders subsequent signal processing and analysis. To obtain a pure blasting vibration signal, a parameter-adaptive variational mode decomposition (VMD) method based on the improved electric eel foraging optimization (EEFO) algorithm is proposed for preprocessing the blasting vibration signal. This method removes high-frequency noise and low-frequency trend components from the signal. Combining the abrupt and related characteristics of blasting vibration signals, a weighted multi-scale permutation entropy is constructed as the fitness function for parameter optimization. The EEFO algorithm, with strong global and local search capabilities, is employed to optimize the VMD decomposition parameters. This approach adaptively determines the optimal combination of decomposition modes
K
and the secondary penalty factor
α
. The analysis results of simulated vibration signals with different interference components and actual measured blasting vibration signals using this method show that, compared to traditional VMD, empirical wavelet transform, and empirical mode decomposition methods, EEFO-VMD has superior adaptability and anti-aliasing capabilities. Even in complex interference components, it can adaptively determine the optimal decomposition parameter combination, effectively removing interference components from the vibration signals. This method is suitable for preprocessing blasting vibration signals. Blasting vibration signals are often contaminated by trend terms and noise, stemming from environments, and instrument errors. This contamination hinders subsequent signal processing and analysis. To obtain a pure blasting vibration signal, a parameter-adaptive variational mode decomposition (VMD) method based on the improved electric eel foraging optimization (EEFO) algorithm is proposed for preprocessing the blasting vibration signal. This method removes high-frequency noise and low-frequency trend components from the signal. Combining the abrupt and related characteristics of blasting vibration signals, a weighted multi-scale permutation entropy is constructed as the fitness function for parameter optimization. The EEFO algorithm, with strong global and local search capabilities, is employed to optimize the VMD decomposition parameters. This approach adaptively determines the optimal combination of decomposition modes K and the secondary penalty factor α. The analysis results of simulated vibration signals with different interference components and actual measured blasting vibration signals using this method show that, compared to traditional VMD, empirical wavelet transform, and empirical mode decomposition methods, EEFO-VMD has superior adaptability and anti-aliasing capabilities. Even in complex interference components, it can adaptively determine the optimal decomposition parameter combination, effectively removing interference components from the vibration signals. This method is suitable for preprocessing blasting vibration signals. Blasting vibration signals are often contaminated by trend terms and noise, stemming from environments, and instrument errors. This contamination hinders subsequent signal processing and analysis. To obtain a pure blasting vibration signal, a parameter-adaptive variational mode decomposition (VMD) method based on the improved electric eel foraging optimization (EEFO) algorithm is proposed for preprocessing the blasting vibration signal. This method removes high-frequency noise and low-frequency trend components from the signal. Combining the abrupt and related characteristics of blasting vibration signals, a weighted multi-scale permutation entropy is constructed as the fitness function for parameter optimization. The EEFO algorithm, with strong global and local search capabilities, is employed to optimize the VMD decomposition parameters. This approach adaptively determines the optimal combination of decomposition modes K and the secondary penalty factor α. The analysis results of simulated vibration signals with different interference components and actual measured blasting vibration signals using this method show that, compared to traditional VMD, empirical wavelet transform, and empirical mode decomposition methods, EEFO-VMD has superior adaptability and anti-aliasing capabilities. Even in complex interference components, it can adaptively determine the optimal decomposition parameter combination, effectively removing interference components from the vibration signals. This method is suitable for preprocessing blasting vibration signals.Blasting vibration signals are often contaminated by trend terms and noise, stemming from environments, and instrument errors. This contamination hinders subsequent signal processing and analysis. To obtain a pure blasting vibration signal, a parameter-adaptive variational mode decomposition (VMD) method based on the improved electric eel foraging optimization (EEFO) algorithm is proposed for preprocessing the blasting vibration signal. This method removes high-frequency noise and low-frequency trend components from the signal. Combining the abrupt and related characteristics of blasting vibration signals, a weighted multi-scale permutation entropy is constructed as the fitness function for parameter optimization. The EEFO algorithm, with strong global and local search capabilities, is employed to optimize the VMD decomposition parameters. This approach adaptively determines the optimal combination of decomposition modes K and the secondary penalty factor α. The analysis results of simulated vibration signals with different interference components and actual measured blasting vibration signals using this method show that, compared to traditional VMD, empirical wavelet transform, and empirical mode decomposition methods, EEFO-VMD has superior adaptability and anti-aliasing capabilities. Even in complex interference components, it can adaptively determine the optimal decomposition parameter combination, effectively removing interference components from the vibration signals. This method is suitable for preprocessing blasting vibration signals. Abstract Blasting vibration signals are often contaminated by trend terms and noise, stemming from environments, and instrument errors. This contamination hinders subsequent signal processing and analysis. To obtain a pure blasting vibration signal, a parameter-adaptive variational mode decomposition (VMD) method based on the improved electric eel foraging optimization (EEFO) algorithm is proposed for preprocessing the blasting vibration signal. This method removes high-frequency noise and low-frequency trend components from the signal. Combining the abrupt and related characteristics of blasting vibration signals, a weighted multi-scale permutation entropy is constructed as the fitness function for parameter optimization. The EEFO algorithm, with strong global and local search capabilities, is employed to optimize the VMD decomposition parameters. This approach adaptively determines the optimal combination of decomposition modes K and the secondary penalty factor α. The analysis results of simulated vibration signals with different interference components and actual measured blasting vibration signals using this method show that, compared to traditional VMD, empirical wavelet transform, and empirical mode decomposition methods, EEFO-VMD has superior adaptability and anti-aliasing capabilities. Even in complex interference components, it can adaptively determine the optimal decomposition parameter combination, effectively removing interference components from the vibration signals. This method is suitable for preprocessing blasting vibration signals. |
| ArticleNumber | 10035 |
| Author | Guo, Lianjun Ren, Fuqiang Zhang, Zuofu Liu, Aobo Xu, Zhenyang Wang, Xuesong |
| Author_xml | – sequence: 1 givenname: Zhenyang surname: Xu fullname: Xu, Zhenyang organization: School of Mining Engineering, University of Science and Technology LiaoNing – sequence: 2 givenname: Zuofu surname: Zhang fullname: Zhang, Zuofu organization: School of Mining Engineering, University of Science and Technology LiaoNing – sequence: 3 givenname: Fuqiang surname: Ren fullname: Ren, Fuqiang email: renfuqiang@ustl.edu.cn organization: School of Civil Engineering, University of Science and Technology LiaoNing – sequence: 4 givenname: Xuesong surname: Wang fullname: Wang, Xuesong organization: School of Mining Engineering, University of Science and Technology LiaoNing – sequence: 5 givenname: Aobo surname: Liu fullname: Liu, Aobo organization: School of Mining Engineering, University of Science and Technology LiaoNing – sequence: 6 givenname: Lianjun surname: Guo fullname: Guo, Lianjun organization: School of Mining Engineering, University of Science and Technology LiaoNing |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40122933$$D View this record in MEDLINE/PubMed |
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| Snippet | Blasting vibration signals are often contaminated by trend terms and noise, stemming from environments, and instrument errors. This contamination hinders... Abstract Blasting vibration signals are often contaminated by trend terms and noise, stemming from environments, and instrument errors. This contamination... |
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| SubjectTerms | 639/166/986 639/166/988 Adaptability Algorithms Decomposition Humanities and Social Sciences multidisciplinary Science Science (multidisciplinary) Signal processing Termites Vibration Wavelet transforms |
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| Title | A parameter adaptive EEFO VMD method to mitigate noise and trend interference of blast vibration signals |
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