Research on an Adaptive Variational Mode Decomposition with Double Thresholds for Feature Extraction

Liu, Hailong; Zhang, Shengjie; Liu, Haodong; Zhao, Huimin; Wu, Jinzhao

doi:10.3390/sym10120684

Cited by 16 publications

(8 citation statements)

References 54 publications

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“…In this paper, the 2D-VMD algorithm is used to decompose the 2D echo signals of different types of human motion by carrier-free UWB radar into several 2D BIMFs mode component. Each BIMF mode component represents the different group centers of the decomposed 2D echo signals and represents an important feature of a certain set of center frequencies of human motion [33,34,35]. This article verifies the 2D-VMD algorithm parameter setting is shown in Table 3, the BIMFs component feature extracted by 2D-VMD algorithm of different types of human 2D echo signal is shown in Figure 5, Figure S1, Figure 6, and Figure S2 is a reconstructed human motion 2D echo signal.…”

Section: Resultsmentioning

confidence: 99%

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition

Jiang

Zhou

Che

et al. 2019

Sensors

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As the size of the radar hardware platform becomes smaller and smaller, the cost becomes lower and lower. The application of indoor radar-based human motion recognition has become a reality, which can be realized in a low-cost device with simple architecture. Compared with narrow-band radar (such as continuous wave radar, etc.), the human motion echo signal of the carrier-free ultra-wideband (UWB) radar contains more abundant characteristic information of human motion, which is helpful for identifying different types of human motion. In this paper, a novel feature extraction method by two-dimensional variational mode decomposition (2D-VMD) algorithm is proposed. And it is used for extracting the primary features of human motion. The 2D-VMD algorithm is an adaptive non-recursive multiscale decomposition method for nonlinear and nonstationary signals. Firstly, the original 2D radar echo signals are decomposed by the 2D-VMD algorithm to capture several 2D intrinsic mode function (BIMFs) which represent different groups of central frequency components of a certain type of human motion. Secondly, original echo signals are reconstructed according to the several BIMFs, which not only have a certain inhibitory effect on the clutter in the echo signal, but can also further demonstrate that the BIMFs obtained by the 2D-VMD algorithm can represent the original 2D echo signal well. Finally, based on the measured ten different types of UWB radar human motion 2D echo analysis signals, the characteristics of these different types of human motion are extracted and the original echo signal are reconstructed. Then, the three indicators of the PCC, UQI, and PSNR between the original echo signals and extraction/reconstruction 2D signals are analyzed, which illustrate the effectiveness of 2D-VMD algorithm to extract feature of human motion 2D echo signals of the carrier-free UWB radar. Experimental results show that BIMFs by 2D-VMD algorithm can well represent the echo signal characteristics of this type of human motion, which is a very effective tool for human motion radar echo signal feature extraction.

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Section: Resultsmentioning

confidence: 99%

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition

Jiang

Zhou

Che

et al. 2019

Sensors

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“…Wavelet transform 1,2 , empirical mode 3,4 , variational mode 5,6 , spectral kurtosis 7,8 are commonly used in mechanical system signal analysis. Among them, the wavelets are known as a "mathematical microscope" and have the characteristics of multi-resolution analysis.…”

Section: Introductionmentioning

confidence: 99%

Research on signal decomposition algorithm of mechanical system based on multiwavelet

Guo

Du²,

Chen³

et al. 2022

6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022)

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Wavelets are referred to as "mathematical microscopes" which can effectively extract the effective state information from the signals of mechanical systems. Multiwavelet is made up of two or more scale functions. It is easier to extract from mechanical system signals than single wavelets. However, in the decomposition of the multiwavelet mechanical system signal, it does not have obvious high and low pass characteristics, and its equivalent filter does not have the ideal frequency domain characteristics. As a result, a frequency band mixing problem will inevitably arise during the decomposition of the mechanical system signal. In order to solve the problem and get a purer decomposed signal, this paper proposes a harmonic wavelet-based band aliasing suppression method. The harmonic wavelet is tightly branched in the frequency domain. It has well-defined functional expression and possesses a fully boxed spectrum that allows the mechanical system signal to be processed in the frequency domain to obtain the desired frequency band information. Therefore, the harmonic wavelet transform can be performed on the mechanical system signals of different frequency bands after multiwavelet decomposition to obtain a purer mechanical system signal in that frequency band. The feasibility and validity of the method are demonstrated through the simulated and measured mechanical system signals, and the comparison with the decomposed mechanical system signals without harmonic wavelet transform shows the superiority of the method and its potential value for application in intelligent maintenance and health diagnosis of energy equipment.

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“…However, the effect of VMD has a lot to do with setting parameters, and it has no self-adaptability. In addition to the need to set parameters for EMD and SSD, there are also problems of modal aliasing and boundary effects [14][15][16][17]. Besides, due to changes in operating conditions, analysis methods such as VMD cannot accurately find fault characteristics of nonlinear signals, and so they cannot effectively reflect fault information [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A New Bearing Fault Diagnosis Method Based on Refined Composite Multiscale Global Fuzzy Entropy and Self‐Organizing Fuzzy Logic Classifier

Zhang

2021

Shock and Vibration

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In this paper, a new feature extraction method called refined composite multiscale global fuzzy entropy (RCMGFE) is proposed. Based on the proposed RCMGFE and self-organizing fuzzy logic classifier (SOF), a new method for bearing fault diagnosis is proposed. Firstly, the fault features of the original bearing signal are extracted by using the proposed refined composite multiscale global fuzzy entropy, and the fault feature set of RCMGFE is constructed on this basis. Secondly, the extracted RCMGFE fault feature set is divided into an offline training sample set, an online training sample set, and a testing sample set. The offline training sample set and the online training sample set are, respectively, input into the offline training stage and the online training stage of the SOF for selecting representative samples and constructing fuzzy rules. Then, the testing sample set is input to the testing stage of the SOF for classification. Finally, the data of drive end bearing and fan end bearing provided by Case Western Reserve University are used to verify the validity of the proposed fault diagnosis method. The experimental results show that, compared with other methods, the proposed fault diagnosis method has a higher classification effect.

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Research on an Adaptive Variational Mode Decomposition with Double Thresholds for Feature Extraction

Cited by 16 publications

References 54 publications

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition

Research on signal decomposition algorithm of mechanical system based on multiwavelet

A New Bearing Fault Diagnosis Method Based on Refined Composite Multiscale Global Fuzzy Entropy and Self‐Organizing Fuzzy Logic Classifier

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