Signals features extraction in liquid-gas flow measurements using gamma densitometry. Part 2: frequency domain

Hanus, Robert; Zych, Marcin; Petryka, Leszek; Jaszczur, Marek; Hanus, Paweł

doi:10.1051/epjconf/201611402036

Cited by 16 publications

(11 citation statements)

References 27 publications

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“…In order to perform automatic pattern flow recognition one may utilize artificial neuron networks or other methods of the so-called calculation intelligence. The particular applications were described in publications [36][37][38][39].…”

Section: Other Possibilities Of Gamma Absorption Methodsmentioning

confidence: 99%

Prospects for the application of radiometric methods in the measurement of two-phase flows

Zych

2018

EPJ Web Conf.

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Abstract. The article constitutes an overview of the application of radiometric methods in the research of two-phase flows: liquid-solid particles and liquid-gas flows. The methods which were used were described on the basis of the experiments which were conducted in the Water Laboratory of the Wrocław University of Environmental and Life Sciences and in the Sedimentological Laboratory of the Faculty of Geology, Geophysics and Environmental Protection, AGH-UST in Kraków. The advanced mathematical methods for the analysis of signals from scintillation probes that were applied enable the acquisition of a number of parameters associated with the flowing two-phase mixture, such as: average velocities of the particular phases, concentration of the solid phase, and void fraction for a liquid-gas mixture. Despite the fact that the application of radioactive sources requires considerable carefulness and a number of state permits, in many cases these sources become useful in the experiments which are presented.

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Section: Other Possibilities Of Gamma Absorption Methodsmentioning

confidence: 99%

Prospects for the application of radiometric methods in the measurement of two-phase flows

Zych

2018

EPJ Web Conf.

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“…Based on the analysis of the data records registered in the experiments the following parameters (features) were calculated for the three types of the flow regimes (classes): "plug flow", "transitional plug -bubble flow", and "bubble flow": a) in the time domain: mean value, variance, standard deviation, 4 th order moment, root mean square (RMS), skewness, kurtosis [19]; b) in the frequency domain: maximum value of the autospectral density function (ADF), maximum value of the cross-spectral density function (CSDF) magnitude, sum and relative sum of the ADF in the selected frequency range, sum and relative sum of the CSDF in the selected frequency range, the maximum value of the sum of selected amplitudes of Short-Time Fourier Transform (STFT) spectrogram, and area and relative area under the graph of STFT spectrogram sum in the selected frequency range [25].…”

Section: Signals Features Extractionmentioning

confidence: 99%

“…The data analysis and computation of the features (predictors) were performed using LabVIEW software. Applied signal processing procedures are described in publications [19,25].…”

Section: Signals Features Extractionmentioning

confidence: 99%

Application of ANN and PCA to two-phase flow evaluation using radioisotopes

et al. 2017

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Abstract. In the two-phase flow measurements a method involving the absorption of gamma radiation can be applied among others. Analysis of the signals from the scintillation probes can be used to determine the number of flow parameters and to recognize flow structure. Three types of flow regimes as plug, bubble, and transitional plug -bubble flows were considered in this work. The article shows how features of the signals in the time and frequency domain can be used to build the artificial neural network (ANN) to recognize the structure of the gas-liquid flow in a horizontal pipeline. In order to reduce the number of signal features the principal component analysis (PCA) was used. It was found that the reduction of signals features allows for building a network with better performance.

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“…There are many non-invasive methods to determine these parameters, such as photon attenuation methods, capacitive tomography, resistance tomography, X-rays, and so on. In recent years, much research has been done to detect these parameters using photon attenuation techniques [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. The researchers in reference [1] studied three types of flow regimes with the names of stratified, homogeneous, and annular in different volume percentages using MCNP code.…”

Section: Introductionmentioning

confidence: 99%

Frequency Domain Feature Extraction Investigation to Increase the Accuracy of an Intelligent Nondestructive System for Volume Fraction and Regime Determination of Gas-Water-Oil Three-Phase Flows

et al. 2021

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In this research, a methodology consisting of an X-ray tube, one Pyrex-glass pipe, and two NaI detectors was investigated to determine the type of flow regimes and volume fractions of gas-oil-water three-phase flows. Three prevalent flow patterns—namely annular, stratified, and homogenous—in various volume percentages—10% to 80% with the step of 10%—were simulated by MCNP-X code. After simulating all the states and collecting the signals, the Fast Fourier Transform (FFT) was used to convert the data to the frequency domain. The first and second dominant frequency amplitudes were extracted to be used as the inputs of neural networks. Three Radial Basis Function Neural Networks (RBFNN) were trained for determining the type of flow regimes and predicting gas and water volume fractions. The correct detection of all flow regimes and the determination of volume percentages with a Mean Relative Error (MRE) of less than 2.02% shows that the use of frequency characteristics in determining these important parameters can be very effective. Although X-ray radiation-based two-phase flowmeters have a lot of advantages over the radioisotope-based ones, they suffer from lower measurement accuracy. One reason might be that the X-ray multi-energy spectrum recorded in the detector has been analyzed in a simple way. It is worth mentioning that the X-ray sources generate multi-energy photons despite radioisotopes that generate single energy photons, therefore data analyzing of radioisotope sources would be easier than X-ray ones. As mentioned, one of the problems researchers have encountered is the lower measurement accuracy of the X-ray, radiation-based three-phase flowmeters. The aim of the present work is to resolve this problem by improving the precision of the X-ray, radiation-based three-phase flowmeter using artificial neural network (ANN) and feature extraction techniques.

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Signals features extraction in liquid-gas flow measurements using gamma densitometry. Part 2: frequency domain

Cited by 16 publications

References 27 publications

Prospects for the application of radiometric methods in the measurement of two-phase flows

Prospects for the application of radiometric methods in the measurement of two-phase flows

Application of ANN and PCA to two-phase flow evaluation using radioisotopes

Frequency Domain Feature Extraction Investigation to Increase the Accuracy of an Intelligent Nondestructive System for Volume Fraction and Regime Determination of Gas-Water-Oil Three-Phase Flows

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