Characterization of Gas–Liquid Two-Phase Flow by Correlation Dimension of Vortex-Induced Pressure Fluctuation

Huang, Sishi; Yin, Jiawen; Sun, Zhiqiang; Li, Saiwei; Zhou, Tian

doi:10.1109/access.2017.2713458

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…For the underwater acoustic signals without prior knowledge, the dynamical behavior of denoised signal is analyzed with noise intensity [ 1 ], correlation dimension [ 37 ] and spatial-dependence recurrence sample entropy (SdrSampEn) [ 38 ] to show the validity of the proposed method. Since the original signal contains a multitude of noises, these feature parameters will be larger than the denoised signal, and we use these feature parameters to evaluate the noise reduction effect.…”

Section: The Proposed Noise Reduction Methodsmentioning

confidence: 99%

“…The given time series of a dynamic system

, (

) is embedded into a m -dimensional space to fully expose the information contained in the time series [ 37 ]. For this phase space, it is converted into a set of vectors as follows:

where

is the time delay,

is the sampling interval,

is the

vector in m -dimensional space,

, and

is the total number of vectors in reconstructed m -dimensional phase space.…”

Section: The Proposed Noise Reduction Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

Yang

2018

Entropy

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Noise reduction of underwater acoustic signals is of great significance in the fields of military and ocean exploration. Based on the adaptive decomposition characteristic of uniform phase empirical mode decomposition (UPEMD), a noise reduction method for underwater acoustic signals is proposed, which combines amplitude-aware permutation entropy (AAPE) and Pearson correlation coefficient (PCC). UPEMD is a recently proposed improved empirical mode decomposition (EMD) algorithm that alleviates the mode splitting and residual noise effects of EMD. AAPE is a tool to quantify the information content of nonlinear time series. Unlike permutation entropy (PE), AAPE can reflect the amplitude information on time series. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by UPEMD. The AAPE of each IMF is calculated. The modes are separated into high-frequency IMFs and low-frequency IMFs, and all low-frequency IMFs are determined as useful IMFs (UIMFs). Then, the PCC between the high-frequency IMF with the smallest AAPE and the original signal is calculated. If PCC is greater than the threshold, the IMF is also determined as a UIMF. Finally, all UIMFs are reconstructed and the denoised signal is obtained. Chaotic signals with different signal-to-noise ratios (SNRs) are used for denoising experiments. Compared with EMD and extreme-point symmetric mode decomposition (ESMD), the proposed method has higher SNR and smaller root mean square error (RMSE). The proposed method is applied to noise reduction of real underwater acoustic signals. The results show that the method can further eliminate noise and the chaotic attractors are smoother and clearer.

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Section: The Proposed Noise Reduction Methodsmentioning

confidence: 99%

“…The given time series of a dynamic system

, (

) is embedded into a m -dimensional space to fully expose the information contained in the time series [ 37 ]. For this phase space, it is converted into a set of vectors as follows:

where

is the time delay,

is the sampling interval,

is the

vector in m -dimensional space,

, and

is the total number of vectors in reconstructed m -dimensional phase space.…”

Section: The Proposed Noise Reduction Methodsmentioning

confidence: 99%

Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

Yang

2018

Entropy

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“…Multiscale Fractal Information Dimension. The original vibration signal with nonlinear and nonstationary characteristics is decomposed into a series of SSDCs by SSD, and combining multiscale analysis and fractal information dimension can realize the quantitative extraction of the complexity and sparsity of the vibration signal from different scales [12].…”

Section: Model Buildingmentioning

confidence: 99%

“…Therefore, the quantitative extraction of fault features is of vital importance. Analyzing the vibration signal from different time scales can obtain the multidimension expressing of signal information, so multiscale analysis is often applied to signal processing [12]. It can highlight the signal feature in different scales more comprehensively and sufficiently, and it not only reflects the global information but also gives attention to the detail information of vibration signal.…”

Section: Introductionmentioning

confidence: 99%

Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN

2019

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Planetary gear is the key part of the transmission system for large complex electromechanical equipment, and in general, a series of degradation states are undergone and evolved into a local fatal fault in its full life cycle. So it is of great significance to recognize the degradation state of planetary gear for the purpose of maintenance repair, predicting development trend, and avoiding sudden fault. This paper proposed a degradation state recognition method of planetary gear based on multiscale information dimension of singular spectrum decomposition (SSD) and convolutional neural network (CNN). SSD can automatically realize the embedding dimension selection and component grouping segmentation, and the original vibration signal being nonlinear and nonstationary can be decomposed into a series of singular spectrum decomposition components (SSDCs), adaptively. Then, the multiscale information dimension which combines multiscale analysis and fractal information dimension is proposed for quantifying and extracting the feature information contained in each SSDC. Finally, CNN is used to achieve the effective recognition of the degradation state of planetary gear. The experimental results show that the proposed method can accurately recognize the degradation state of planetary gear, and the overall recognition rate is up to 97.2%, of which the recognition rate of normal planetary gear reaches 100%.

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“…During the experiment, they proved SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive. A method combining artificial neural networks with related dimensions to construct novel gas-liquid flow pattern diagrams to distinguish between the bubble, bubble/plug transitional, plug, slug, and annular flows (Huang et al 2017). A method based on combination of multi-beam gamma ray attenuation and dual-modal density measurement technology used radial basis function (RBF) neural network for identifying the flow pattern and determining the void fraction in gas-liquid two-phase flows independent of the liquid phase changes of gas-liquid twophase flow (Roshani et al 2017).…”

Section: Introductionmentioning

confidence: 99%

A novel method to identify the flow pattern of oil–water two-phase flow

Fan

2020

J Petrol Explor Prod Technol

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This paper presents a novel method combining extreme learning machine (ELM) and multiple empirical mode decomposition (MEMD) to identify flow patterns of oil–water two-phase flow. The proposed method can recognize accurately five typical flow patterns of horizontal oil–water two-phase flow. Taking the Lorenz system as an example, we verify the MEMD is more suitable for simultaneous decomposition of multi-channel signals than empirical mode decomposition and ensemble empirical mode decomposition. In the proposed method, we employ the MEMD to decompose the multivariate conductance signal of oil–water two-phase flow to obtain the same intrinsic mode function modes, select the normalized energy of the high-frequency components as the eigenvalue, and utilize the trained ELM to achieve a good recognition result. The experimental results show that the proposed method is not only fast and generalized, but also has high accuracy in identifying flow patterns of oil–water two-phase flow.

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Characterization of Gas–Liquid Two-Phase Flow by Correlation Dimension of Vortex-Induced Pressure Fluctuation

Cited by 10 publications

References 29 publications

Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN

A novel method to identify the flow pattern of oil–water two-phase flow

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