Two-dimensional Time-frequency Plane Research Articles

Experimental Investigations of Offshore Sand Production Monitoring Based on the Analysis of Vibration in Response to Weak Shocks

Sand production is a problem that is often encountered in unconventional oil and gas exploitation and that is difficult to effectively solve. Accurate online monitoring of sand production is one of the keys to ensuring the safety and long-term production of oil wells as well as efficient production throughout the life cycle of production wells. This paper proposes a method for monitoring sand production in offshore oil wells that is based on the vibration response characteristics of sand-carrying fluid flow impinging on the pipe wall. This method uses acceleration sensors to obtain the weak vibration response characteristics of sand particles impinging on the pipe wall on a two-dimensional time-frequency plane. The time-frequency parameters are further optimized, and the ability to identify weakly excited vibration signals of sand particles in the fluid stream is enhanced. The difference between the impact response of the sand particles and the impact response of the fluid flow to the pipe wall is identified, and corresponding indoor verification experiments are carried out. Under different sand contents, particle sizes, and flow rates (sand content 0-2‰, sand particle size 96-212 μm, and flow velocity 1-3 m/s), the impact response frequency of sand particles to the pipe wall exhibits good consistency. The characteristic frequency band of sand impacting the pipe wall is 30-50 kHz. A statistical method is used to establish the response law of the noise signal of the fluid. Based on this knowledge, a real-time calculation model of sand production in offshore oil wells is constructed, and the effectiveness of this model is verified. Finally, a field test is carried out with a self-developed sand production signal dynamic time-frequency response software system on 4 wells of an oil production platform in the Bohai Sea. This system can effectively distinguish sand-producing wells from non-sand-producing wells. The dynamic time-frequency response, field test results, and actual laboratory results are consistent, verifying the effectiveness of the method proposed in this paper and further providing a theory for improving the effectiveness of the sand production monitoring method under complex multiphase flow conditions. This study also provides technical guidance for the industrial application of sand production monitoring devices in offshore oil wells.

Real-Time Implementation of a Neural Integrator Backstepping Control via Recurrent Wavelet First Order Neural Network

Wavelets are designed to have compact support in both time and frequency, giving them the ability to represent a signal in the two-dimensional time–frequency plane. The Gaussian, the Mexican hat, and the Morlet wavelets are crude wavelets that can be used only in continuous decomposition. The Morlet wavelet is complex-valued and suitable for feature extraction using continuous wavelet transform. Continuous wavelets are favoured when a high temporal resolution is required at all scales. In this paper, considering the properties from the Morlet wavelet and based on the structure of a recurrent high-order neural network model, a novel wavelet neural network structure, here called recurrent wavelet first-order neural network, is proposed in order to achieve a better identification of the behavior of dynamic systems. The effectiveness of our proposal is explored through the design of a centralized neural integrator backstepping control scheme for a two degree-of-freedom robot manipulator evolving in the vertical plane. The performance of the overall neural identification and control scheme is verified through numerical simulation using the mathematical model for a benchmark prototype. Moreover, real-time results validate the effectiveness of our proposal when using a robotic arm, of our own design, powered by industrial servomotors.

A wavelet neural control scheme for a quadrotor unmanned aerial vehicle

Wavelets are designed to have compact support in both time and frequency, giving them the ability to represent a signal in the two-dimensional time-frequency plane. The Gaussian, the Mexican hat and the Morlet wavelets are crude wavelets that can be used only in continuous decomposition. The Morlet wavelet is complex-valued and suitable for feature extraction using the continuous wavelet transform. Continuous wavelets are favoured when high temporal and spectral resolution is required at all scales. In this paper, considering the properties from the Morlet wavelet and based on the structure of a recurrent high-order neural network model, a novel wavelet neural network structure, here called a recurrent Morlet wavelet neural network, is proposed in order to achieve a better identification of the behaviour of dynamic systems. The effectiveness of our proposal is explored through the design of a decentralized neural backstepping control scheme for a quadrotor unmanned aerial vehicle. The performance of the overall neural identification and control scheme is verified via simulation and real-time results.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.

Assessment of Debonding in Concrete Slabs Using Seismic Methods

A combination of impact echo (IE) and ultrasonic surface waves techniques was used to locate the debonding in concrete slabs at several locations on TX-225 near Houston, Texas. A portable seismic pavement analyzer (PSPA), a hand-held automated system, was used for seismic data collection in the field. Field measurements are presented in the time and frequency domains in the form of time records and amplitude spectra. The characteristics differentiating the response (time records) of fully bonded and debonded slabs are discussed. The frequency domain (spectral) analysis was shown to be superior to time domain analysis in describing the response of slabs in marginal condition (i.e., slabs showing partial debonding or horizontal cracks). On the basis of the spectral characteristics of their surface response, the slabs were categorized into four different categories: good, fair, poor, and bad conditions (in terms of debonding). Time–frequency analysis was proposed as a complementary tool for the analysis of IE signals. The test records were analyzed by a time–frequency analysis method and are presented in a two-dimensional time–frequency plane. The advantages of using a time–frequency technique over a spectral analysis are described. Finally, the debonding assessments made on the basis of PSPA measurements were verified by ground truth data.

Interpretation of Transient Electromagnetic Field Data Using Joint Time-Frequency Analysis

Using the joint time-frequency (TF) analysis method, we interpret transient electromagnetic (TEM) data so as to fully employ information contained in TEM signals. We choose joint TF distribution (or representation) of Wigner-Will distribution and Gabor expansion to transform the one-dimensional time-domain signal to the two-dimensional TF plane, which not only distinguishes responses of different geo-electric sections from each other successfully, but also provides clear physical meaning between exhibited signal feature and electrical structure of the stratum. This describes the transient eddy field excitation and underground attenuation process in time and frequency respects. The fitting difference is reduced and the precision of quantitative interpretation is improved by means of joint TF inversion with time and frequency window data.

Characterization of pressure fluctuation in swirling gas-solid two-phase flow in a horizontal pipe

Abstract In order to analyze the features of pressure fluctuation and to interpret particle flow pattern transitions in both Fourier and physical spaces, the experimental wall pressure fluctuations in swirling gas-solid two-phase flows are analyzed by statistical analysis and wavelet transform. Our experiment is carried out in a horizontal pipe with a length of 7.5 m and an inner diameter of 76 mm. The initial swirl number based on the total inflow was varied from 0.0 to 0.61, the mean gas velocity was varied from 6 to 28 m/s and the solid mass flow rate was varied from 0.08 to 0.5. We discovered that the amplitude of pressure fluctuation in the axial gas-solid flow is larger than that of the swirling gas-solid flow at low gas velocity and the amplitude of the swirling gas-solid flow decreases when increasing the initial swirl number within the range of low gas velocity. With increasing initial swirl intensity, the strong peaks of the power spectral density function (PSD) become smaller and the magnitudes of PSD decrease as a whole. We can say that the swirling gas-solid flow is very effective in reducing the unstable flow. From the wavelet analysis of pressure fluctuations, the unsteady characteristics of swirling gas-solid two-phase flows were revealed over a two-dimensional time-frequency plane. The distribution of wavelet coefficients indicates the fluctuation of particle velocities and concentrations with time and scales. At low air velocities, a periodic oscillation appears in wavelet coefficients around 0.5 Hz, which may be interpreted as the existence of dunes along the bottom of the pipe at nearly constant intervals. The dominant frequency of the suspension flow increases along the flow direction and reaches 110 Hz in the fully developed region.

Noninvasive early detection of focal cerebral ischemia.

Describes analyzing somatosensory evoked potentials for detecting cerebral injury in its earlier phases. Cerebral vascular diseases (CVDs) are significant factors in causing morbidity and mortality. Early diagnosis is the key to rapid recovery and improve prognosis. Most CVDs are associated with brain ischemia/hypoxia, making ischemic cerebral vascular disease a major concern. A noninvasive technique for early detection of focal brain ischemic injury is a major need. Here the authors discuss a technique to help meet this need. They present a model of SD rat focal cerebral ischemia. A new high-resolution time-frequency analysis method-the discrete Cabor spectrogram-is introduced to analyze somatosensory evoked potential signals for early noninvasive detection of brain focal ischemic injury. The time-varying spectrum of the SEP is displayed as a contour map on the two-dimensional time-frequency plane. The difference between ischemic and normal regions can be clearly seen on this map, which may be useful for diagnostic purposes.