scholarly journals Recursive Variational Mode Decomposition Algorithm for Real Time Power Signal Decomposition

2015 ◽  
Vol 21 ◽  
pp. 540-546 ◽  
Author(s):  
Soman K.P. ◽  
Prabaharan Poornachandran ◽  
Athira S. ◽  
Harikumar K.
Keyword(s):  
Real Time ◽  
Signal Decomposition ◽  
Decomposition Algorithm ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

Fine-tuned variational mode decomposition for fault diagnosis of rotary machinery

2019 ◽  
Vol 19 (5) ◽  
pp. 1453-1470
Author(s):  
Ali Dibaj ◽  
Mir Mohammad Ettefagh ◽  
Reza Hassannejad ◽  
Mir Biuok Ehghaghi
Keyword(s):  
Principal Component ◽  
Signal Decomposition ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Signal Processing Technique ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Optimal Values ◽  
The Mean ◽  
Mixing Problem

Variational mode decomposition is a powerful signal processing technique that can adaptively decompose a multi-component signal into a number of modes, via solving an optimization problem. The optimal performance of this method in signal decomposition and avoiding of the mode mixing problem strictly relies on the true selection of decomposition parameters, that is, the number of extracted modes ( K) and the mode frequency bandwidth control parameter ( α). In the literature, the optimal values of these parameters are achieved by evaluating fault-related indices like kurtosis, but such an index is inefficient in judging the decomposition of healthy (without fault-related components), low-defective, and high-noise signals. In this research, a novel method called fine-tuned variational mode decomposition is proposed to determine the optimal values of decomposition parameters K and α, by judging the adaptive indices. In this proposed method, the optimal values of these parameters are obtained by minimizing the mean bandwidth of the extracted modes. In order to achieve these optimal values, the mean correlation coefficients between the adjacent modes and the energy loss coefficient between the original signal and the reconstructed signal, should not exceed of defined thresholds for optimal values. The proposed method is applied to the simulation signal and experimental ones collected from the automobile gearbox system. Comparing this method with those in the literature exhibits its higher effectiveness in the true decomposition of signals with different natures. It is also shown that using the proposed method for signal decomposition is able to correctly classify the healthy and defective states of the gearbox system alongside the principal component analysis method and support vector machine classifier.

Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising

2021 ◽  
pp. 014233122110636
Author(s):  
Dongmei Wang ◽  
Lijuan Zhu ◽  
Jikang Yue ◽  
Jingyi Lu ◽  
Gongfa Li
Keyword(s):  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Decomposition Algorithm ◽  
Signal Denoising ◽  
Two Dimensional ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Whale Optimization

To eliminate noise interference in pipeline leakage detection, a signal denoising method based on an improved variational mode decomposition algorithm is proposed. This work adopts a standard variational mode decomposition algorithm with decomposition level K and the penalty factor α. The improvements consist of using a two-dimensional sparrow search algorithm to find K and α. To verify the superiority of the sparrow search algorithm to find K and α, it is compared with three earlier studies. These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable.

scholarly journals Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification

2019 ◽  
Author(s):  
Vinícius R. Carvalho ◽  
Márcio F.D. Moraes ◽  
Antônio P. Braga ◽  
Eduardo M.A.M. Mendes
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Empirical Mode Decomposition ◽  
Decomposition Methods ◽  
Seizure Detection ◽  
Variational Mode Decomposition ◽  
Real Time Processing ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Adaptive Decomposition

AbstractSignal processing and machine learning methods are valuable tools in epilepsy research, potentially assisting in diagnosis, seizure detection, prediction and real-time event detection during long term monitoring. Recent approaches involve the decomposition of these signals in different modes or functions in a data-dependent and adaptive way. These approaches may provide advantages over commonly used Fourier based methods due to their ability to work with nonlinear and non-stationary data. In this work, three adaptive decomposition methods (Empirical Mode Decomposition, Empirical Wavelet Transform and Variational Mode Decomposition) are evaluated for the classification of normal, ictal and inter-ictal EEG signals using a freely available database. We provide a previously unavailable common methodology for comparing the performance of these methods for EEG seizure detection, with the use of the same classifiers, parameters and spectral and time domain features. It is shown that the outcomes using the three methods are quite similar, with maximum accuracies of 97.5% for Empirical Mode Decomposition, 96.7% for Empirical Wavelet Transform and 98.2% for Variational Mode Decomposition. Features were also extracted from the original non-decomposed signals, yielding inferior, but still fairly accurate (95.3%) results. The evaluated decomposition methods are promising approaches for seizure detection, but their use should be judiciously analysed, especially in situations that require real-time processing and computational power is an issue. An additional methodological contribution of this work is the development of two python packages, already available at the PyPI repository: One for the Empirical Wavelet Transform (ewtpy) and another for Variational Mode Decomposition (vmdpy).

scholarly journals A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

2019 ◽  
Vol 255 ◽  
pp. 02017 ◽  
Author(s):  
M. Firdaus Isham ◽  
M. Salman Leong ◽  
M. H. Lim ◽  
M. K. Zakaria
Keyword(s):  
Decomposition Method ◽  
Rotating Machinery ◽  
Signal Decomposition ◽  
Future Research ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal ◽  
Mixing Problem ◽  
Conventional Signal

Signal processing method is very important in most diagnosis approach for rotating machinery due to non-linearity, non-stationary and noise signals. Recently, a new adaptive signal decomposition method has been proposed by Dragomiretskiy and Zosso known as variational mode decomposition (VMD). The VMD method has merit in solving mode mixing problem in most conventional signal decomposition method. This paper aims to review the applications of the VMD method in rotating machinery diagnosis. The advantages and limitations of the VMD method are discussed. Current solution on VMD limitation also have been review and discussed. Lastly, the future research suggestion has been pointed out in order to enhance the performance of the VMD method on rotating machinery diagnosis.

Sign in / Sign up

Export Citation Format

Share Document