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.

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 Noisy Reverberation Suppression Using AdaBoost Based EMD in Underwater Scenario

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Kusma Kumari Cheepurupalli ◽  
Raja Rajeswari Konduri
Keyword(s):  
Signal Processing ◽  
Acoustic Signal ◽  
Probability Of Detection ◽  
Chirp Signal ◽  
Processing Scheme ◽  
Adaptive Boosting ◽  
Mode Decomposition ◽  
Crucial Problem ◽  
Filtering Technique ◽  
Simulation Results

Reverberation suppression is a crucial problem in sonar communications. If the acoustic signal is radiated in the water as medium then the degradation is caused due to the reflection coming from surface, bottom, and volume of water. This paper presents a novel signal processing scheme that offers an improved solution in reducing the effect of interference caused due to reverberation. It is based on the combination of empirical mode decomposition (EMD) and adaptive boosting (AdaBoost) techniques. AdaBoost based EMD filtering technique is used for reverberation corrupted chirp signal to decrease the noisy components present in the received signal. An improvement in the probability of detection is achieved using the proposed algorithm. The simulation results are obtained for various reverberation times at various SNR levels.

scholarly journals A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Cancan Yi ◽  
Yong Lv ◽  
Zhang Dang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Pso Algorithm ◽  
Potential Method ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Swarm Optimization ◽  
Mode Decomposition

Variational mode decomposition (VMD) is a new method of signal adaptive decomposition. In the VMD framework, the vibration signal is decomposed into multiple mode components by Wiener filtering in Fourier domain, and the center frequency of each mode component is updated as the center of gravity of the mode’s power spectrum. Therefore, each decomposed mode is compact around a center pulsation and has a limited bandwidth. In view of the situation that the penalty parameter and the number of components affect the decomposition effect in VMD algorithm, a novel method of fault feature extraction based on the combination of VMD and particle swarm optimization (PSO) algorithm is proposed. In this paper, the numerical simulation and the measured fault signals of the rolling bearing experiment system are analyzed by the proposed method. The results indicate that the proposed method is much more robust to sampling and noise. Additionally, the proposed method has an advantage over the EMD in complicated signal decomposition and can be utilized as a potential method in extracting the faint fault information of rolling bearings compared with the common method of envelope spectrum analysis.

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.

scholarly journals A Multi‐Granularity Density Peak Clustering Algorithm Based on Variational Mode Decomposition

2021 ◽  
Vol 30 (4) ◽  
pp. 658-668
Author(s):  
GU Ziwen ◽  
LI Peng ◽  
LANG Xun ◽  
YU Yixuan ◽  
SHEN Xin ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Variational Mode Decomposition ◽  
Density Peak ◽  
Mode Decomposition ◽  
Density Peak Clustering

scholarly journals Analysis of the sound signal to fault detection of bearings based on Variational Mode Decomposition

2021 ◽  
Vol 942 (1) ◽  
pp. 012020
Author(s):  
Hamid Shiri ◽  
Jacek Wodecki
Keyword(s):  
Fault Detection ◽  
Damage Detection ◽  
Acoustic Signal ◽  
Noise Signal ◽  
Special Treatment ◽  
Variational Mode Decomposition ◽  
Test Rig ◽  
Vibration Signals ◽  
Vibration Data ◽  
Mode Decomposition

Abstract Damage detection in rotating machines is well established for vibration signals. Unfortunately, there are situations, where usage of vibration is not possible. Then, acoustic signal could be used instead. Unfortunately, usually acoustic signal are more noisy and require special treatment for obtain successful damage detection. In the paper we propose to use Variational mode decomposition (VMD) to omit noise for finding de-noise signal. We use vibration data to validate acoustic signal based procedure. The experiment was done on test rig with damaged bearings.

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.

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