Adaptive Optimal Kernel Time-frequency Representation Research Articles

A study of oil–water two-phase flow pattern recognition based on thermal sensors

To understand the flow characteristics and kinetic modelling of oil–water two-phase flow(OWTPF), this paper employs thermal array sensors to identify the flow pattern (FP) in OWTPF horizontal pipelines. Firstly, the thermal array sensor installation was constructed. Secondly, the time-varying characteristics of multi-feature signals were qualitatively analyzed using the adaptive optimal kernel time–frequency representation (AOK-TFR) method and unthresholded recurrence plot (URP) method. Finally, the sparrow search algorithm-support vector machine (SSA-SVM) machine learning algorithm is used for quantitative prediction of multi-feature classification, which leads to high prediction accuracy. The experiment proves that the correct rate of the thermal method in recognizing six types of OWTPF is above 91%, the correct rate of five types of flow is above 97.33%. This paper adopts the thermal method to identify the multiphase FP, which is a major innovation in this paper and also lays the foundation for recognizing the flow characteristics of multiphase flow.

Analysis of Cavitation-induced Vibration Characteristics of a Vortex Pump Based on Adaptive Optimal Kernel Time-frequency Representation

Cavitation-induced vibration presents a significant challenge in vortex pumps, leading to potential damage to hydraulic components and adverse effects on pump performance. This study aims to investigate the long-term implications of such phenomena. To capture the vibration signals caused by cavitation, we utilized vibration acceleration sensors on the vortex pump and collected data at five predetermined measuring points under three different operating conditions. The analysis used two prominent techniques, fast Fourier transform (FFT) and adaptive optimal kernel time-frequency representation (AOK-TFR), to explore the frequency-domain and time-frequency characteristics of the vibration signals. The findings reveal a notable increase in frequency amplitude at each monitoring point as the flow rate rises. Under cavitation conditions, pronounced vibration characteristics are observed along the y-axis and z-axis of the volute, with maximum vibration intensities of 1.83 m/s² and 1.80 m/s², respectively. The frequency amplitude exhibits non-constant behavior in the time series. Moreover, variations in the time-frequency characteristics are identified with changing flow rates. A distinct signal with a frequency of 750 Hz manifests in the x-axis and y-axis of the volute when the head experiences a 3% reduction from the cavitation level.

Characterizing gas-liquid two-phase flow behavior using complex network and deep learning.

Gas-liquid two-phase flow is polymorphic and unstable, and characterizing its flow behavior is a major challenge in the study of multiphase flow. We first conduct dynamic experiments on gas-liquid two-phase flow in a vertical tube and obtain multi-channel signals using a self-designed four-sector distributed conductivity sensor. In order to characterize the evolution of gas-liquid two-phase flow, we transform the obtained signals using the adaptive optimal kernel time-frequency representation and build a complex network based on the time-frequency energy distribution. As quantitative indicators, global clustering coefficients of the complex network at various sparsity levels are computed to analyze the dynamic behavior of various flow structures. The results demonstrate that the proposed approach enables effective analysis of multi-channel measurement information for revealing the evolutionary mechanisms of gas-liquid two-phase flow. Furthermore, for the purpose of flow structure recognition, we propose a temporal-spatio convolutional neural network and achieve a classification accuracy of 95.83%.

Instability of Low-Velocity and High Water-Cut Oil–Water Two-Phase Flow Based on Microwave Signals

Accurately understanding the nonlinear and complex dynamics underlying low-velocity and high water-cut oil–water two-phase flow is a significant but challenging problem to the study of flow pattern boundary transformation and flow heat transfer. In this article, we propose a nonlinear analysis method to characterize and distinguish patterns from low-velocity and high water-cut oil–water two-phase flow based on the fractional entropy algorithm that have found great usage in complex science. First, we obtain the variable measurement and images of the oil–water two-phase flow by a microwave sensor and a high-speed camera, respectively. Second, we analyze the complexity of flow structure using recurrence algorithms and adaptive optimal kernel time–frequency representation. Finally, we propose a dynamic nonlinear analysis framework to characterize flow instability based on multiscale fractional weighted permutation entropy. The results show that the entropy rate and its mean value are sensitive to the flow structure. These interesting and significant findings suggest that the multiscale fractional weighted permutation entropy can potentially be a powerful tool for uncovering the underlying dynamics leading to the formation of two-phase flow.

Analysis of parallel mini-channels’ complex flow boiling and dryout dynamics based on the pressure drop signals

Based on a heat exchange experiment, the authors used R141b as the working medium, and analyzed the differential pressure signal of parallel mini-channels (N = 9, L = 250 mm, Wc = 2 mm, H = 2 mm). Different flow patterns (bubble flow, slug flow, annular flow, dryout, mist flow) have different pressure drops. Flow boiling characteristics were analyzed by the adaptive optimal kernel time–frequency representation (AOK-TFR), autoregression (AR) power spectrum and recurrence plot (RP). Then, the dryout kinetics of working fluids were revealed. Finally, through the combination of three recursive characteristic parameters, the dryout prediction based on the differential pressure signal was proposed, which provides the theoretical basis for the dynamic characteristics of boiling heat transfer. At the same time, it gives a solution for prevention of the second type of heat transfer deterioration in a project.

Fault Detection for the Scraper Chain Based on Vibration Analysis Using the Adaptive Optimal Kernel Time-Frequency Representation

A scraper conveyor is a key component of large-scale mechanized coal mining equipment, and its failure patterns are mainly caused by chain jam and chain fracture. Due to the difficulties with direct measurement for multiple performance parameters of the scraper chain, this paper deals with a novel strategy for fault detection of the scraper chain based on vibration analysis of the chute. First, a chute vibration model (CVM) is applied for modal analysis, and the hammer impact test (HIT) is conducted to validate the accuracy of the CVM; second, the measuring points for vibration analysis of the chute are determined based on the modal assurance criterion (MAC); and third, to simulate the actual vibration properties of the chute, a dynamic transmission system model (DTSM) is constructed based on finite element modeling. The fixed-point experimental testing (FPET) is then conducted to indicate the correctness of simulation results. Subsequently, the DTSM-based vibration responses of the chute under different operating conditions are obtained. In this paper, the proposed strategy is employed to determine the occurrence of chain faults by amplitude comparisons, while failure patterns are distinguished by the adaptive optimal kernel time-frequency representation (AOKR).

Instability of horizontal oil-water flows based on the signal-dependent complex admittance representations

Horizontal oil-water two-phase flows are characterized by unstable motions of interfacial wave and entrained droplets. An experiment of horizontal oil-water flow was made in an acrylic pipe with an inner diameter of 20 mm. A measurement system of concave capacitance sensor is designed to detect the real and imaginary parts of flow complex admittance. An Adaptive Optimal Kernel Time-Frequency Representation (AOK TFR) of the complex admittance angle is used to characterize the flow instability in terms of total energy. A detrended cross-correlation analysis (DCCA) of the oil-water complex admittance is conducted to investigate the instability multi-scale flow structures. The cross-correlation coefficients at large and small scales enable to characterize the instability of the oil-water interface and the entrained droplets in continuous phases, respectively. The result suggests that the DCCA cross-correlation coefficients and the total energy extracted from AOK TFR are accordant in uncovering the instability of horizontal oil-water flows.

An adaptive optimal-Kernel time-frequency representation-based complex network method for characterizing fatigued behavior using the SSVEP-based BCI system

The Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system has seen extensively applications in many fields, such as physical recovery of handicap persons, obstacle avoidance of intelligent vehicles, entertainment and smart homes. However, subjects easily get fatigued because of the involving long-time operations. The presence of fatigue symptoms typically affect the efficiency of the BCI system, so investigating the effects of fatigue on the SSVEP classification accuracy from the perspective of brain network becomes a challenging issue of significant importance. In this paper, we develop an adaptive optimal-Kernel time-frequency representation (AOK-TFR)-based complex network method for characterizing fatigued behavior using the SSVEP-based BCI system. We apply the traditional Canonical Correlation Analysis (CCA) and Fisher Linear Discriminant Analysis (FLDA) to classify SSVEP signals. We find that the classification accuracy at the fatigue states is significantly lower than that at the normal states. To reveal the reasons, we infer and analyze the AOK-TFR-based functional brain network with SSVEP signals. In particular, we calculate the AOK-TFR of the acquired 30-channel SSVEP signals under both normal and fatigue conditions and then construct a brain network in terms of the two-norm distance between different channels. Our results suggest that the small-world-ness of the network at normal states is prominent, and the main brain regions associated with SSVEP are in the prefrontal cortex and occipital lobe. Our analysis sheds new insights into the understanding and management of the fatigued behavior using the SSVEP-based BCI system.

Analysis of vertical upward oil-gas-water three-phase flow based on multi-scale time irreversibility

Based on the measurement signals obtained from oil-gas-water three-phase flow experiments, multi-scale time irreversibility is investigated to characterize the dynamic behavior of different flow patterns. Firstly, the conductance fluctuation signals of oil-gas-water three-phase flow in the vertical upward pipe are collected. Subsequently, three typical flow patterns, i.e., oil in water-type bubble flow, oil in water-type transition flow and oil in water-type slug flow, are defined and analyzed. Using multi-scale time irreversibility index and the surrogate data approach, multi-scale time irreversibility can be detected from the collected signals with different flow patterns. Furthermore, in order to quantitatively characterize the time irreversibility of oil-gas-water three-phase flow, a novel criterion, i.e., Average value of Asymmetric Distance (AAD), is proposed in this paper. The proposed criterion is validated by the experiments. The experiment results show that the conductance signals acquired from different typical flow patterns of oil-gas-water three-phase flow have different time irreversibility. The AAD value obtained for the oil in water-type slug flow is larger than the AAD value obtained for the oil in water-type transition flow, and the AAD value obtained for the oil in water-type transition flow is larger than the AAD value obtained for the oil in water-type bubble flow. In addition, the mechanism leading to the formation of time irreversibility is further explored in terms of adaptive optimal kernel time-frequency representation. It is proved that AAD can be used to reveal the characteristics of asymmetry in the evolution of oil-gas-water three-phase flow pattern. The utilization of AAD will provide an important reference for characterization and analysis of the non-linear dynamic characteristics of oil-gas-water three-phase flow regime evolution in real applications.

Short-time Fourier transform with the window size fixed in the frequency domain

The Short-Time Fourier Transform (STFT) is widely used to convert signals from the time domain into a time–frequency representation. This representation has well-known limitations regarding time–frequency resolution. In this paper we use the basic concept of the Short-Time Fourier Transform, but fix the window size in the frequency domain instead of in the time domain. This approach is simpler than similar existing methods, such as adaptive STFT and multi-resolution STFT, and in particular it requires neither the band-pass filter banks of multi-resolution techniques, nor the evaluation of local signal characteristics of adaptive techniques. Three case studies are analyzed and the results show that the proposed method allows better identification of signal components compared to standard STFT, multi-resolution STFT and Adaptive Optimal-Kernel Time Frequency Representation, although the method is not computationally efficient in its present form. Some synthetic and real world signals are used to demonstrate the effectiveness of the proposed technique.

Characterization of non-stationary properties of vehicle–bridge response for structural health monitoring

Quantifying the non-stationary properties of bridge under passing vehicle has been an important topic in structural health monitoring of bridge. There are many methods of time–frequency representation used for the study of dynamics of bridge under passing vehicle, including spectrogram, wavelet, Hilbert–Huang transform, and so on. This article uses adaptive optimal kernel time–frequency representation to quantify the non-stationary properties of the response of bridge under passing vehicle and illustrates and discusses its advantages over conventional time–frequency methods.

Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG.

Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant importance. Combining adaptive optimal kernel time-frequency representation and visibility graph, we develop a novel method for detecting epileptic seizure from EEG signals. We construct complex networks from EEG signals recorded from healthy subjects and epilepsy patients. Then we employ clustering coefficient, clustering coefficient entropy and average degree to characterize the topological structure of the networks generated from different brain states. In addition, we combine energy deviation and network measures to recognize healthy subjects and epilepsy patients, and further distinguish brain states during seizure free interval and epileptic seizures. Three different experiments are designed to evaluate the performance of our method. The results suggest that our method allows a high-accurate classification of epileptiform EEG signals.

Measurement of droplet sizes in bubbly oil-in-water flows using a fluid-sampling device

Abstract One important characteristic of bubbly oil-in-water two-phase flows is the droplet size distribution, which has a significant effect on the heat/mass transfer between phases and the turbulence characteristics. In this study, a sampling device of dynamic fluid is designed to trap the morphological structures of the bubbly oil-in-water flows with low flow rate and high water cut. In the experiment, oil droplets in vertical upward pipe are forced into a sampling slot which is specifically designed to ensure that the passing oil droplets are not overlapped. The images of the oil droplets are recorded using a high speed camera when the mixture flows through the sampling slot. The methods of multi-scale edge detection and watershed segmentation are employed to extract the edges of the solitary and adjoining oil droplets in the flow images. Based on the detected oil droplet edges the equivalent oil droplet diameters are derived and compared with a model of maximum droplet diameter. Additionally, time-frequency entropy and the total energy of measured conductance signals are extracted using Adaptive Optimal Kernel Time-Frequency Representation (AOK TFR) to characterize the droplet size distribution of bubbly oil-water two-phase flows.

Response Characteristics of Coaxial Capacitance Sensor for Horizontal Segregated and Non-Uniform Oil-Water Two-Phase Flows

In this paper, the response characteristics of a coaxial capacitance sensor for horizontal oil-water two-phase flows with segregated and non-uniform phase distribution are investigated. First, the experiment of horizontal oil-water two-phase flow is carried out in a 20-mm inner diameter pipe with a flow concentration device. The response signals of the coaxial capacitance sensor under different flow patterns are collected by a data acquisition device. Meanwhile, the liquid holdup is measured using three pairs of parallel-wire capacitance probes and quick closing valve technology to uncover the complex slippage behaviors between phases. Then, the effects of the flow slippage and non-uniform phase distribution on the sensor response characteristics are investigated based on the equivalent impedance circuit analysis and adaptive optimal kernel time-frequency representation. In general, the results show that the coaxial capacitance sensor presents preferable response resolution for selected horizontal oil-water two-phase flow patterns, such as stratified flow and stratified flow with mixing at interface. However, as the flow pattern evolves to dispersed oil-in-water and water flow with high water-cut, the response resolution of the coaxial capacitance sensor is lower.

A texture-based rolling bearing fault diagnosis scheme using adaptive optimal kernel time frequency representation and uniform local binary patterns

This paper presents a new scheme for rolling bearing fault diagnosis using texture features extracted from the time–frequency representations (TFRs) of the signal. To derive the proposed texture features, firstly adaptive optimal kernel time frequency representation (AOK-TFR) is applied to extract TFRs of the signal which essentially describe the energy distribution characteristics of the signal over time and frequency domain. Since the AOK-TFR uses the signal-dependent radially Gaussian kernel that adapts over time, it can exactly track the minor variations in the signal and provide an excellent time–frequency concentration in noisy environment. Simulation experiments are furthermore performed in comparison with common time–frequency analysis methods under different noisy conditions. Secondly, the uniform local binary pattern (uLBP), which is a computationally simple and noise-resistant texture analysis method, is used to calculate the histograms from the TFRs to characterize rolling bearing fault information. Finally, the obtained histogram feature vectors are input into the multi-SVM classifier for pattern recognition. We validate the effectiveness of the proposed scheme by several experiments, and comparative results demonstrate that the new fault diagnosis technique performs better than most state-of-the-art techniques, and yet we find that the proposed algorithm possess the adaptivity and noise resistance qualities that could be very useful in real industrial applications.

Water holdup measurement of oil-water two-phase flow with low velocity using a coaxial capacitance sensor

This paper is devoted to measuring water holdup of oil-water two-phase flow with low velocity using a coaxial capacitance sensor. We first investigated the distribution characteristics of the coaxial capacitance sensor via the finite element method (FEM) and optimized its geometry size. Afterward, we carried out a vertical upward oil-water two-phase flow experiment in a pipe with a 20-mm inner diameter (ID) to obtain the responses of four flow patterns: transition flow (TF), dispersed oil-in-water slug flow (D OS/W), dispersed oil-in-water flow (D O/W) and very fine dispersed oil-in-water flow (VFD O/W). The sensor outputs were normalized to equivalent water holdup and compared with the set values from a quick closing valve (QCV) method. Finally, we studied the time-frequency dynamic characteristics of different flow patterns based on the Adaptive Optimal Kernel Time-Frequency Representation (AOK-TFR) algorithm to further analyze the evolution of oil-water two-phase flow with low velocity. The results show that the coaxial capacitance sensor can be effectively applied in the measurement of water holdup under moderate and low water-cuts as well as uncovering discrepancies regarding different flow patterns.

Multi-Scale Morphological Analysis of Conductance Signals in Vertical Upward Gas–Liquid Two-Phase Flow

Abstract The multi-scale analysis is an important method for detecting nonlinear systems. In this study, we carry out experiments and measure the fluctuation signals from a rotating electric field conductance sensor with eight electrodes. We first use a recurrence plot to recognise flow patterns in vertical upward gas–liquid two-phase pipe flow from measured signals. Then we apply a multi-scale morphological analysis based on the first-order difference scatter plot to investigate the signals captured from the vertical upward gas–liquid two-phase flow loop test. We find that the invariant scaling exponent extracted from the multi-scale first-order difference scatter plot with the bisector of the second-fourth quadrant as the reference line is sensitive to the inhomogeneous distribution characteristics of the flow structure, and the variation trend of the exponent is helpful to understand the process of breakup and coalescence of the gas phase. In addition, we explore the dynamic mechanism influencing the inhomogeneous distribution of the gas phase in terms of adaptive optimal kernel time–frequency representation. The research indicates that the system energy is a factor influencing the distribution of the gas phase and the multi-scale morphological analysis based on the first-order difference scatter plot is an effective method for indicating the inhomogeneous distribution of the gas phase in gas–liquid two-phase flow.

Nonlinear multi-scale dynamic stability of oil–gas–water three-phase flow in vertical upward pipe

Characterizing stability and nonlinearity underlying oil–water–gas three-phase flow is a challenging problem of significant importance. We carry out experiments and measure the fluctuation signals from a rotating electric field conductance sensor with eight electrodes. We use recurrence plot and adaptive optimal kernel time–frequency representation to recognize different oil–water–gas three-phase flow patterns from experimental measurements. Then we employ multi-scale weighted complexity entropy causality plane (MS-WCECP) to explore the nonlinear characteristics for five typical oil–water–gas three-phase flow structures. The results suggest that our method enables to indicate flow pattern transitions. In particular, with the increase of scales, more information will be lost. Slug flow ends up in chaotic region, representing high complexity; Churn flow falls down from the chaotic to the random noise area, indicating the decreasing stability; while the drop degree of bubble flow is the biggest, suggesting that bubble flow has the most randomness. These findings demonstrate that multi-scale weighted complexity entropy causality plane can effectively depict the transitions of three-phase flow structures and serve as a useful tool for probing the nonlinear dynamics of the three-phase flows.

Differential pressure method for measuring water holdup of oil–water two-phase flow with low velocity and high water-cut

This paper is devoted to measuring water holdup of oil–water two-phase flow with low velocity and high water-cut through differential pressure method. At first, we carry out vertical upward oil–water two-phase flow experiment in a 20mm inner diameter pipe to obtain the response of differential pressure sensor, and use Vertical Multiple Electrode Array conductance sensor (VMEA) as well as Adaptive Optimal Kernel Time–Frequency Representation (AOK TFR) algorithm to identify three different flow patterns, i.e., dispersed oil-in-water slug flow (D OS/W), dispersed oil-in-water flow (D O/W) and very fine dispersed oil-in-water flow (VFD O/W). Then the water holdup is extracted from the differential pressure sensor response by calculating frictional gradient loss, and the water holdup measured is compared with the set value by using the method of quick closing valve. Finally, we using drift-flux model predict the individual phase superficial velocities based on the flow pattern classification and water holdup. The research results show that differential pressure method proves to be an effective approach to investigate flow characteristics in oil–water two-phase flow with low velocity and high water-cut.

The AOK-TFR analysis of high temperature gas–solid two-phase flow

Due to the increasing scarcity of coal resources, and a drop in the quality of existing power plant coal, much research figure out best configuration of fluidized beds are required. Against this backdrop, this paper uses lignite coal to examine the heating delivery and spray dynamic characteristics of spouted bed. Unlike existing literature focusing on the research of signals of any fixed point under a fixed condition, this paper tests the signals under dynamic conditions that is, with the air temperature constantly rising. The dynamic temperature and pressure fluctuation signals of fixed points were measured and researched. The reason we chose to research dynamic conditions is that the temperature rising process are dynamic. This paper presents an effective quadric time–frequency representations of signal; namely, the Adaptive Optimal Kernel Time–Frequency Representation (AOK TFR) combined with spectral energy and entropy. The analysis focuses on the internal mechanism of pressure fluctuation signals corresponding to different temperature gradient distribution. We also did a regression analysis of the bed temperature and pressure drop. The results suggest that the AOK TFR method can reveal flow characteristics and spray mechanisms of high-temperature gas–solid two-phase flow from different analysis points, and that this method is an effective nonlinear time–frequency method to analyze other two-phase flow processes. There is a high correlation between drops in pressure and temperature when the operating temperature is between 140°C and 350°C.