Comparison of variational mode decomposition and empirical wavelet transform methods on EEG signals for motor imaginary applications

Krishnan, K. Keerthi; Soman, K P

doi:10.1504/ijbet.2022.121740

Cited by 4 publications

(2 citation statements)

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“…It was proved that the proposed method could be effective for fault diagnosis. Krishnan and Soman [13] proposed an optimized EWT-based identification method for the problem of EEG signal identification. The process investigated timedependent desynchronization and used variational modal decomposition incorporating EWT for multiple electrode EEG signals for analysis and extraction.…”

Section: Related Workmentioning

confidence: 99%

Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform

Cheng

2023

J. meas. eng.

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As the continuous growth of the machinery industry, the importance of rolling bearings as key connecting parts in machinery movement is also increasing. However, the extraction and diagnosis of rolling bearing fault signals are difficult, and how to use modern transform analysis methods to raise the extraction efficiency and diagnostic accuracy becomes the focus. For this, a rolling bearing fault signal extraction and diagnosis model is designed based on empirical wavelet transform. The diagnostic model is optimized by using support vector machine and quantum genetic algorithm to design a rolling bearing fault signal extraction and diagnosis model based on improved empirical wavelet transform-support vector machine. The test results show that the research method can obtain four component signals showing different anomalies when generating time domain diagrams. Only five component peaks are generated and one group is extracted as output when generating component peaks. The abnormal amplitude of envelope spectrum basically reaches 0.40×10-6 or above. The judgment accuracy of component diagnosis reaches 98.12%. The above results show that the research method has better fault signal extraction ability and better diagnostic accuracy when performing fault signal diagnosis, which can provide new technical support for rolling bearing fault signal extraction and diagnosis.

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Section: Related Workmentioning

confidence: 99%

Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform

Cheng

2023

J. meas. eng.

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“…The method is theoretically complete, computationally efficient, adaptive, and can effectively separate nonlinear non-smooth signals. All these make it a good choice for multi-component signal decomposition [9][10][11][12][13]. Xin et al [14,15] used the EWT to extract the dense modal parameters of a pedestrian bridge and the time-varying modal parameters of a highway bridge.…”

Section: Introductionmentioning

confidence: 99%

Identification of bridge modal parameters from GNSS data by integrating IEWT and robust ICA algorithm

Yu,

Xie,

Fang

et al. 2024

Meas. Sci. Technol.

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Empirical Wavelet Transform (EWT) is often used to process GNSS bridge deformation monitoring data, but it leads to inaccurate band division and too many spurious modes. To address these problems, this study proposes an improved algorithm to identify bridge modal parameters, which combines improved EWT (IEWT) and Robust ICA. The proposed method adopts the autoregressive power spectrum of an improved covariance algorithm, instead of the Fourier spectrum, for band division. Additionally, it performs noise reduction and reconstruction of multi-channel GNSS monitoring data. The reconstructed signal is inputted as multi-channel observation signal into Robust ICA to extract features of the source signal. Finally, the natural excitation technique and Hilbert transform are used to solve the self-oscillation frequency and damping ratio of the structure. The proposed method is validated using both simulation data and the GNSS monitoring data of the Wilford suspension bridge. The results show that the proposed method can effectively reduce the measurement noise and successfully identify the first-order vibration frequencies and damping ratios of bridge. This algorithm can also be applied in the parameter identification of other engineering structures from GNSS data.

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Diagnosis of Voltage Dips Using a Novel Morphological Filter and a Smart Deep Learning LSTM-Based Minimum Variance RVFLN Classifier

Priyadarshini

Dash

2022

Iran J Sci Technol Trans Electr Eng

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Comparison of variational mode decomposition and empirical wavelet transform methods on EEG signals for motor imaginary applications

Cited by 4 publications

References 0 publications

Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform

Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform

Identification of bridge modal parameters from GNSS data by integrating IEWT and robust ICA algorithm

Diagnosis of Voltage Dips Using a Novel Morphological Filter and a Smart Deep Learning LSTM-Based Minimum Variance RVFLN Classifier

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