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.

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.

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.

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.

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.

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.

Experimental flow pattern map, slippage and time–frequency representation of oil–water two-phase flow in horizontal small diameter pipes

We detect the flow structures of a horizontal oil–water two-phase flow in a 20mm inner-diameter pipe using 8-channels radial mini-conductance probes. In particular, we present an experimental flow pattern map that includes 218 flow conditions and compare this map to the flow pattern transitional boundaries predicted by published models. In addition, using the Adaptive Optimal Kernel Time–Frequency Representation, we analyze the conductance fluctuating signals and characterize the flow pattern in terms of the total energy and dominant frequency. Based on the liquid holdup measurements using the quickly closing valve technology combined with three parallel-wire capacitance probes, we investigate the slip effect between the oil and water phases under various flow conditions. The results show that the flow structures greatly affect the slippage, and the slip ratio is sensitive to flow pattern variations.

Adaptive Optimal Kernel Time–Frequency Representation Technique for Partial Discharge Ultra-high-frequency Signals Classification

An intelligent system for automatic partial discharge pattern recognition is proposed using adaptive optimal kernel time-frequency representation and a fuzzy k-nearest neighbor classifier. The adaptive optimal kernel technique is employed to acquire the joint time-frequency information for partial discharge signals, which is characterized by the adaptive optimal kernel amplitude matrix. A new feature extraction algorithm, i.e., non-negative matrix factorization aided principal component analysis, is proposed to solve the difficulties of principal component analysis for feature extraction of partial discharge adaptive optimal kernel amplitude matrices due to the high dimensionality. Using an ultra-high frequency detector, 600 partial discharge signals sampled from 4 categories of typical artificial defect models in the laboratory are employed for testing. It is shown that the maximum classification accuracy of 94.33% is obtained considering different non-negative matrix factorization parameter r and various non-negative matrix factorization iterations T. Also, the classification performance of the non-negative matrix factorization–principal component analysis features is superior to that of principal component analysis features extracted from original partial discharge signals, two-dimensional non-negative matrix factorization features and phase-resolved partial discharge statistical operators. The proposed technique can be used for partial discharge pattern recognition based on ultra-high-frequency detection arrangements.

Complex networks from experimental horizontal oil–water flows: Community structure detection versus flow pattern discrimination

Abstract We propose a complex network-based method to distinguish complex patterns arising from experimental horizontal oil–water two-phase flow. We first use the adaptive optimal kernel time–frequency representation (AOK TFR) to characterize flow pattern behaviors from the energy and frequency point of view. Then, we infer two-phase flow complex networks from experimental measurements and detect the community structures associated with flow patterns. The results suggest that the community detection in two-phase flow complex network allows objectively discriminating complex horizontal oil–water flow patterns, especially for the segregated and dispersed flow patterns, a task that existing method based on AOK TFR fails to work.

Flow pattern map and time–frequency spectrum characteristics of nitrogen–water two-phase flow in small vertical upward noncircular channels

We experimentally investigate the vertical upward gas–liquid two-phase flow in a 2×0.81mm small rectangular channel and an equilateral triangle channel with a 2mm side length. The two channels have same hydraulic diameters (Dh=1.15mm). We first present an experimental flow pattern map with nitrogen and water superficial velocities ranging from 0.08m/s to 11.82m/s and 0.12m/s to 1.52m/s, respectively. We also compare the flow pattern transition criteria between the triangle and rectangular cross sections by using the same hydraulic radius. We employ the influence rule that small passage geometry limits the flow pattern transition criteria. Thereafter, we comparatively analyze the transition boundaries of experiment flow patterns with other results in literature and classical prediction models. Results show that the cross-sectional shapes and experimental conditions of the experimental pipeline significantly affect the changes in the flow regime. Given the differential pressure signal of the two-phase flow, we propose two effective quadric time–frequency representations, namely, the adaptive optimal kernel time–frequency representation (AOK TFR) and data reduction sub-frequency band wavelet (DA SFBW) to investigate the complex behavior of vertical upward gas–liquid flows. We extract the positive power spectral density of the singular value–frequency entropy, singular value–frequency entropy, damping ratio, and vibration mode to characterize the evolution of flow patterns. The results suggest that the AOK TFR method can reveal flow dynamic details, whereas the DA SFBW based method is a powerful tool for characterizing the dynamical characteristics of different vertical upward gas–liquid flow patterns.

Seismic attenuation qualitative characterizing method based on adaptive optimal-kernel time–frequency representation

Abstract The main issue of seismic attenuation characterizing method based on time–frequency analyzing method is the time–frequency resolution. The adaptive optimal-kernel time–frequency representation, which has high time–frequency resolution comparing with commonly used time–frequency analyzing method, is investigated. The seismic attenuation qualitative characterizing method based on adaptive optimal-kernel time–frequency representation is proposed. The synthetic data example and 3D field-data example reveal that the proposed method can qualitatively characterize seismic attenuation, and the attenuated anomaly of this field-data example coincides with gas reservoir well.

Experimental investigation of pressure instabilities affected by cavitation for a double-suction centrifugal pump

Cavitation is a common fault which has a close relationship with pressure instabilities in centrifugal pumps. In order to investigate the hydroacoustic response to cavitation phenomena, a pressure fluctuation experimental is carried out on a double-suction centrifugal pump. Frequency spectrum analysis and time-frequency joint analysis of pressure fluctuations in semi-spiral suction chamber and volute casing are presented based on fast Fourier transform (FFT) and adaptive optimal-kernel time-frequency representation (AOK TFR) methods. The results show that in semi-spiral suction chamber, as the available NPSH value continuously decreases, the frequencies affected by cavitation transfer to a lower frequency range and become relative higher concentrate in time-frequency representation. In volute casing, blade passage frequency (BPF), specific frequency at 1/2 of blade passage frequency (147 Hz) and some frequency bands are well captured. The amplitudes of BPF and BPF/2 are shown to have a reasonable dependence on available NPSH value, though the measuring location relative to volute tongue should be taken into account. Strong pulsation characteristics are observed on relative low available NPSH value conditions. Pressure fluctuation at impeller rotating frequency is shown to be sensitive with cavitation process, of which the amplitude sharply increases at first and then decreases.

Flow pattern and water holdup measurements of vertical upward oil–water two-phase flow in small diameter pipes

We experimentally investigate vertical upward oil–water two-phase flow in a 20mm inner diameter pipe. We first using vertical multiple electrode array conductance sensor measure the water holdup, and using mini-conductance probes define five observed flow patterns, i.e., very fine dispersed oil-in-water (VFD O/W) flow, dispersed oil-in-water (D O/W) flow, oil-in-water slug (D OS/W) flow, water-in-oil (D W/O) and transition flow (TF). Then we present an experimental flow pattern map with oil and water superficial velocity ranging from 0.258m/s to 3.684m/s and 0.184m/s to 1.474m/s, respectively. In addition, we obtain the flow pattern transition boundaries in terms of water holdup. Finally, we propose an effective quadric time–frequency representation, i.e., the adaptive optimal kernel time–frequency representation (AOK TFR) to investigate the complex behavior underlying vertical upward oil–water flow. In particular, we extract total energy and time–frequency entropy to characterize the evolutions of flow patterns. The results suggest that AOK TFR based method could potentially be a powerful tool for characterizing the dynamical characteristics of different vertical upward water-dominant oil–water flow patterns.

Experimental Investigation of Time-Frequency Characteristics of Pressure Fluctuations in a Double-Suction Centrifugal Pump

Pressure fluctuation is the primary reason for unstable operations of double-suction centrifugal pumps. By using flush mounted pressure transducers in the semispiral suction chamber and the volute casing of a double-suction pump, the pressure fluctuation signals were obtained and recorded at various operating conditions. Spectral analyses were performed on the pressure fluctuation signals in both frequency domain and time-frequency domain based on fast Fourier transform (FFT) and an adaptive optimal-kernel time-frequency representation (AOK TFR). The results show that pressure fluctuations at the impeller rotating frequency and some lower frequencies dominated in the semispiral suction chamber. Pressure fluctuations at the blade passing frequency, the impeller rotating frequency, and their harmonic frequencies were identified in the volute casing. The amplitude of pressure fluctuation at the blade passing frequency significantly increased when the flow rate deviated from the design flow rate. At 107% of the design flow rate, the amplitude increased more than 254% than that at the design flow rate. The time-frequency characteristics of these pressure fluctuations were affected greatly by both operating conditions and measurement locations. At partial flow rates the pulsation had a great irregularity and the amplitudes at the investigated frequencies were much larger than ones at the design flow rate. An asymmetrical pressure fluctuation structure in the volute casing was observed at all flow rates. The pulsation behavior at the blade passing frequency was the most prominent near the volute tongue zone, and the pressure waves propagated in both the radial and circumferential directions.

An adaptive optimal-kernel time-frequency representation

Time-frequency representations with fixed windows or kernels figure prominently in many applications, but perform well only for limited classes of signals. Representations with signal-dependent kernels can overcome this limitation. However, while they often perform well, most existing schemes are block-oriented techniques unsuitable for on-line implementation or for tracking signal components with characteristics that change with time. The time-frequency representation developed in the present paper, based on a signal-dependent radially Gaussian kernel that adapts over time, surmounts these difficulties. The method employs a short-time ambiguity function both for kernel optimization and as an intermediate step in computing constant-time slices of the representation. Careful algorithm design provides reasonably efficient computation and allows on-line implementation. Certain enhancements, such as cone-kernel constraints and approximate retention of marginals, are easily incorporated with little additional computation. While somewhat more expensive than fixed kernel representations, this new technique often provides much better performance. Several examples illustrate its behavior on synthetic and real-world signals.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A signal-dependent time-frequency representation: fast algorithm for optimal kernel design

A time-frequency representation based on an optimal, signal-dependent kernel has been previously been proposed in an attempt to overcome one of the primary limitations of bilinear time-frequency distributions: that the best kernel and distribution depend on the signal to be analyzed. The optimization formulation for the signal-dependent kernel results in a linear program with a unique feature: a tree structure that summarizes a set of constraints on the kernel. The authors present a fast algorithm based on sorting to solve a special class of linear programs that includes the problem of interest. For a kernel with Q variables, the running time of the algorithm is O(Q log Q), which is several orders of magnitude less than any other known method for solving this class of linear program. This efficiency enables the computation of the signal-dependent, optimal-kernel time-frequency representation at a cost that is on the same order as a fixed-kernel distribution. An important property of the optimal kernel is that it takes on essentially only the values of 1 and 0.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>