Complex variational mode decomposition for signal processing applications

2017 ◽  
Vol 86 ◽  
pp. 75-85 ◽  
Author(s):  
Yanxue Wang ◽  
Fuyun Liu ◽  
Zhansi Jiang ◽  
Shuilong He ◽  
Qiuyun Mo
Keyword(s):  
Signal Processing ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

scholarly journals A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2949
Author(s):  
Changpeng Li ◽  
Tianhao Peng ◽  
Yanmin Zhu
Keyword(s):  
Signal Processing ◽  
Acoustic Signal ◽  
Clustering Algorithm ◽  
Pso Algorithm ◽  
Variational Mode Decomposition ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Drum Shearer ◽  
Best Parameter ◽  
And Cluster Analysis

During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

scholarly journals Iterative variational mode decomposition and extreme learning machine for gearbox diagnosis based on vibration signals

2019 ◽  
Vol 13 (1) ◽  
pp. 4477-4492
Author(s):  
M. Firdaus Isham ◽  
M. Salman Leong ◽  
L. M. Hee ◽  
Z. A. B. Ahmad
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Processing Method ◽  
Mode Number ◽  
Variational Mode Decomposition ◽  
Vibration Signals ◽  
Signal Processing Method ◽  
Mode Decomposition ◽  
Learning Machine

Vibration-based monitoring and diagnosis provide an excellent and reliable monitoring strategies for maintaining and sustaining a million dollars of industrial assets. The signal processing method is one of the key elements in gearbox fault diagnosis for extracting most useful information from raw vibration signals. Variational mode decomposition (VMD) is one of the recent signal processing methods that helps to solve many limitations in traditional signal processing method. However, pre-determine the input parameters especially the mode number become a challenging task for using this method. Then, this study aims to propose an iterative approach for selecting the mode number for the VMD method by using the normalized mean value (NMV) plot. The NMV value is calculates based on the ratio of a summation of VMD modes and the input signals. The result shows that the proposed iterative VMD approach can select an accurate mode number for the VMD method. Then, the vibration signals decomposed into different VMD modes and used for gearbox fault diagnosis. Statistical features have been extracted from the selected VMD modes and pass into extreme learning machine (ELM) for fault classification. Iterative VMD-ELM provide significance improvement of about 20% higher accuracy in classification result as compared with EMD-ELM. Hence, this research study offers a new mean for gearbox diagnosis strategy.  

scholarly journals A Method for Determining Intrinsic Mode Function Number in Variational Mode Decomposition and Its Application to Bearing Vibration Signal Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Shoujun Wu ◽  
Fuzhou Feng ◽  
Junzhen Zhu ◽  
Chunzhi Wu ◽  
Guang Zhang
Keyword(s):  
Signal Processing ◽  
Great Influence ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Center Frequency ◽  
Noise Robustness ◽  
Variational Mode Decomposition ◽  
Normalized Mutual Information ◽  
Mode Function ◽  
Mode Decomposition

Variational mode decomposition (VMD) method has been widely used in the field of signal processing with significant advantages over other decomposition methods in eliminating modal aliasing and noise robustness. The number (usually denoted by K) of intrinsic mode function (IMF) has a great influence on decomposition results. When dealing with signals including complex components, it is usually impossible for the existing methods to obtain correct results and also effective methods for determining K value are lacking. A method called center frequency statistical analysis (CFSA) is proposed in this paper to determine K value. CFSA method can obtain K value accurately based on center frequency histogram. To shed further light on its performance, we analyze the behavior of CFSA method with simulation signal in the presence of variable components amplitude, components frequency, and components number as well as noise amplitude. The normal and fault vibration signals obtained from a bearing experimental setup are used to verify the method. Compared with maximum center frequency observation (MCFO), correlation coefficient (CC), and normalized mutual information (NMI) methods, CFSA is more robust and accurate, and the center frequencies results are consistent with the main frequencies in FFT spectrum.

Post-processing of ultrasonic signals for the analysis of defects in wind turbine blade using guided waves

2018 ◽  
Vol 53 (8) ◽  
pp. 546-555 ◽  
Author(s):  
Kumar Anubhav Tiwari ◽  
Renaldas Raisutis
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Guided Waves ◽  
Wind Turbine Blade ◽  
Discrete Wavelet ◽  
Variational Mode Decomposition ◽  
Ultrasonic Signals ◽  
Mode Decomposition

In this work, the most promising ultrasonic signal processing methods—discrete wavelet transform, variational mode decomposition and Hilbert transform—are applied for the analysis of disbond-type defects in the segment of wind turbine blade. Two disbond-type artificial defects having diameters of 81 and 25 mm were located on the main spar of wind turbine blade. The low-frequency ultrasonic system developed by Ultrasound Research Institute of the Kaunas University of Technology was used for the experimental investigation of wind turbine blade using guided waves and only one side of the blade segment was accessed. Two contact type ultrasonic transducers separated by 50 mm distance and fixed on a movable mechanical panel were used as a transmitter–receiver pair during the experiment for the ultrasonic signals recording up to the scanning distance of 250 mm with the scanning step of 1 mm. Both types of defects were marginally detected from the conventional experimental B-scan and therefore appropriate signal processing techniques were used to improve the accuracy of the analysis of defects. The discrete wavelet transform was combined with the amplitude detection method for estimating the size and location of defects. Finally, the variational mode decomposition is combined with the Hilbert transform to compare the instantaneous frequencies and amplitudes of the defect-free and defective signals as well as for the measurement of time-delays between the defect-free and defective signals.

scholarly journals Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Hongping Hu ◽  
Yan Ao ◽  
Huichao Yan ◽  
Yanping Bai ◽  
Na Shi
Keyword(s):  
Signal Processing ◽  
Hybrid Algorithm ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Signal Denoising ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Swarm Optimization ◽  
Mode Decomposition ◽  
Vector Hydrophone

To eliminate the noise from the signals received by MEMS vector hydrophone, a joint algorithm is proposed in this paper based on wavelet threshold (WT) denoising, variational mode decomposition (VMD) optimized by a hybrid algorithm of Multiverse Optimizer (MVO) and Particle Swarm Optimization (PSO), and correlation coefficient (CC) judgment to perform the signal denoising of MEMS vector hydrophone, named as MVO-PSO-VMD-CC-WT, whose fitness function is the root mean square error (RMSE) and whose individual is the parameters of VMD. For every individual, the original signal is decomposed by VMD into pure components, noisy components, and noise components in terms of CC judgment, where the pure components are directly retained, the noisy components are denoised by WT denoising, and the noise components are discarded, and then, the denoised noisy components and the pure components are reconstructed to be the denoised signal of the original signal. Then, the obtained optimal individual is utilized to perform the signal denoising by MVO-PSO-VMD-CC-WT by the use of the above repeated signal processing. Two simulated experimental results show that the MVO-PSO-VMD-CC-WT algorithm which has the highest signal-to-noise ratio and the least RMSE is superior to the other compared algorithms. And the proposed MVO-PSO-VMD-CC-WT algorithm is effectively applied to perform the signal denoising of the actual lake experiments. Therefore, the proposed MVO-PSO-VMD-CC-WT is suitable for the signal denoising and can be applied into the actual experiments in signal processing.

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