Identification of wrist movements based on magnetoencephalograms via noise assisted multivariate empirical mode decomposition

2022 ◽  
Vol 72 ◽  
pp. 103307
Author(s):  
Zifeng Chen ◽  
Bingo Wing-Kuen Ling
Keyword(s):  
Empirical Mode Decomposition ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition

scholarly journals Multivariate empirical mode decomposition–based structural damage localization using limited sensors

2021 ◽  
pp. 107754632110069
Author(s):  
Sandeep Sony ◽  
Ayan Sadhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Cost Effective ◽  
Threshold Value ◽  
Damage Localization ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Percentage Difference ◽  
Mode Decomposition

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time–frequency methods to delineate such minor global damage.

scholarly journals Early Fault Detection Method for Rotating Machinery Based on Harmonic-Assisted Multivariate Empirical Mode Decomposition and Transfer Entropy

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 873 ◽  
Author(s):  
Zhe Wu ◽  
Qiang Zhang ◽  
Lixin Wang ◽  
Lifeng Cheng ◽  
Jingbo Zhou
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Rotating Machinery ◽  
Transfer Entropy ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Coupling Characteristics ◽  
High Frequency Harmonic ◽  
Frequency Harmonic

It is a difficult task to analyze the coupling characteristics of rotating machinery fault signals under the influence of complex and nonlinear interference signals. This difficulty is due to the strong noise background of rotating machinery fault feature extraction and weaknesses, such as modal mixing problems, in the existing Ensemble Empirical Mode Decomposition (EEMD) time–frequency analysis methods. To quantitatively study the nonlinear synchronous coupling characteristics and information transfer characteristics of rotating machinery fault signals between different frequency scales under the influence of complex and nonlinear interference signals, a new nonlinear signal processing method—the harmonic assisted multivariate empirical mode decomposition method (HA-MEMD)—is proposed in this paper. By adding additional high-frequency harmonic-assisted channels and reducing them, the decomposing precision of the Intrinsic Mode Function (IMF) can be effectively improved, and the phenomenon of mode aliasing can be mitigated. Analysis results of the simulated signals prove the effectiveness of this method. By combining HA-MEMD with the transfer entropy algorithm and introducing signal processing of the rotating machinery, a fault detection method of rotating machinery based on high-frequency harmonic-assisted multivariate empirical mode decomposition-transfer entropy (HA-MEMD-TE) was established. The main features of the mechanical transmission system were extracted by the high-frequency harmonic-assisted multivariate empirical mode decomposition method, and the signal, after noise reduction, was used for the transfer entropy calculation. The evaluation index of the rotating machinery state based on HA-MEMD-TE was established to quantitatively describe the degree of nonlinear coupling between signals to effectively evaluate and diagnose the operating state of the mechanical system. By adding noise to different signal-to-noise ratios, the fault detection ability of HA-MEMD-TE method in the background of strong noise is investigated, which proves that the method has strong reliability and robustness. In this paper, transfer entropy is applied to the fault diagnosis field of rotating machinery, which provides a new effective method for early fault diagnosis and performance degradation-state recognition of rotating machinery, and leads to relevant research conclusions.

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