Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

The W transform and its improved methods for time-frequency analysis of seismic data

The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.

Restructuring Tilth Layers Can Change the Microbial Community Structure and Affect the Occurrence of Verticillium Wilt in Cotton Field

Restructuring tilth layers(RTL)is a tillage method that exchanges the 0–20 and 20–40 cm soil layers that can be applied during cotton cultivation to increase cotton yield,eliminate weeds and alleviate severe disease,including Verticillium wilt.However,the mechanism by which RTL inhibits Verticillium wilt is unclear.Therefore,we investigated the distribution of microbial communities after rotary tillage(CK)and RTL treatments to identify the reasons for the reduction of Verticillium wilt in cotton fields subjected to RTL.Illumina high-throughput sequencing was used to sequence the bacterial and fungal genes.The disease incidence and severity of Verticillium wilt decreased by 28.57%and 42.64%,respectively,after RTL.Moreover,RTL significantly enhanced bacterial richness and evenness at 20–40 cm and-reduced the differences in fungal evenness and richness between soil depths of 0–20 and 20–40 cm.The number of Verticillium dahliae decreased,while the relative abundance of biocontrol bacteria such as Bacillus and Pseudoxanthomonas increased significantly following RTL.Overall RTL improved bacterial diversity,decreased the number of Verticillium dahliae and increased the relative abundance of biocontrol bacteria,which may have suppressed the occurrence of Verticillium wilt in cotton fields.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Study on the Urbanization-based Restructuring of Urban Spaces in Meizhou City of China

By introducing the urbanization history of Meizhou City,major problems of its spatial structure were summarized as small scale of urban areas and incomplete hierarchical structure;poor cohesive force of central city,imbalanced regional spatial ratio;disorderly development of urban spaces;and fierce competition for space.Then spatial restructuring of Meizhou City was discussed from the following 3 perspectives:optimizing hierarchical structure of space,planning in advance to construct a central city with more than 1 million residents;establishing the traffic hub of Guangdong,Fujian and Jiangxi;improving spatial structure of industries.It was to enhance sustainable social and economic development of the local area,break the dualistic structure and realize the overall development of urban and rural areas,and also narrow the urban-rural gap.

Application of sparse time-frequency decomposition to seismic data

The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results.

TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft

Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.

Intelligibility evaluation of enhanced whisper in joint time-frequency domain

Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyzed. Experimental results show that for a spectrum of some density, the joint time-frequency gain-modification based speech enhancement algorithm achieves significant improvement in intelligibility. Additionally, the spectrum region where the estimated spectrum is smaller than the clean spectrum, is the most important region contributing to intelligibility improvement for the enhanced whisper. The spectrum region where the estimated spectrum is larger than twice the size of the clean spectrum is detrimental to speech intelligibility perception within the whisper context.

Effects of Gabor transform parameters on signa time-frequency resolution

In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.

Financial Strategy during Power System Restructuring

Aiming at the problems of power structure irrationality, lagging of power network behind power source development and high proportion of overdone and small sized units existing currently in power system in China, financial and running strategies are set forth corresponding to power system restructuring.

The causes and environmental effects of land use conversion during agricultural restructuring in Northeast China

During the last decade of the 20th century, extensive conversion inagricultural land use took place in Northeast China. The goal of this study is to ascertain itsspatial distribution and regional differentiation, determine its causes, and analyze itsenvironmental impact. Especially we attempt to elucidate how institutional constraints havefacilitated the change at a time of agrarian restructuring when newly emerging free market washybridized with the former planned economy. Information on six categories of land use was mappedfrom interpretation of Landsat TM images recorded in 1990, 1995 and 2000. Most of land use changestook place during the first half of the decade, coinciding with abrupt and chaotic changes ingovernment directives. Farmland was changed mainly to woodland, water body and built-up areas whilewoodland and grassland were converted chiefly to farmland. Spatially, the change from farmland towoodland was restricted to the west of the study area. The change from grassland to farmland tookplace in the grazing and farming interlocked west. These chaotic and occasionally conflictingchanges were largely caused by lack of stability and consistency in agricultural land use policiespromulgated. They have exerted adverse impacts on the local environment, including land degradation,increased flooding, and modified climate regime.

Time-Frequency Signal Processing for Gas-Liquid Two Phase Flow Through a Horizontal Venturi Based on Adaptive Optimal-Kernel Theory

A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed,and then the AOK theory was used to analyze the dynamic differ-ential pressure signal.The mechanism of two-phase flow was discussed through the time-frequency spectrum.On the condition of steady water flow rate,with the increasing of gas flow rate,the flow pattern changes from bubbly flow to slug flow,then to plug flow,meanwhile,the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band;moreover,when the flow pattern is slug flow,there are two wave peaks showed in the time-frequency spectrum.Finally,a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK.When the characteristic variables were visu-ally analyzed,the relationship between different combination of characteristic variables and flow patterns would be gotten.The results show that,this method can explain the law of flow in different flow patterns.And characteristic variables,defined by this method,can get a clear description of the flow information.This method provides a new way for the flow pattern identification,and the percentage of correct prediction is up to 91.11%.

Grain boundary restructuring and La/Ce/Y application in Nd–Fe–B magnets

Since the 1980 s, Nd–Fe–B with largest energy product(BH)max approaching the theoretical limit has become the landmark of permanent magnetic material.The application spectrum for Nd–Fe–B continues to expand over time both in the industrial and commercial sectors, which leads to growing research interests for solving the long-standing drawbacks of Nd–Fe–B, i.e., poor corrosion resistance, low coercivity, high Dy/Tb and low La/Ce/Y consumption.Concerning the above obstacles, we aim to present the novel grain boundary restructuring(GBR) approach, from GB design, processing, to structure evolution and property evaluation with a focus on the corrosion and coercivity mechanism of the restructured 2:14:1-typed magnets.Starting with an introduction to the fundamental of GBR, two representative examples,high-electrode-potential(Pr, Nd)32.5Fe62.0Cu5.5 and low-melting-point Dy71.5Fe28.5, are given with detailed descriptions of the advantages of GBR to enhance the intrinsic anti-corrosion stability and to strengthen the coercivity at low Dy consumption.Microstructure–property correlations are established to understand the critical importance of regulating the restructured GB phase to maximize the all-round performance of the 2:14:1-typed permanent magnets.Aiming at sustainable and balanced development of rare earth(RE) industry, the proceeding section proposes new prototypes of La–Ce and Y–Ce co-substitutions with dual benefits of stabilizing the 2:14:1 tetragonal phase and strengthening the intrinsic hard magnetism.The findings of additional REFe2 intergranular phase delight that the GBR approach also opens up a new horizon of research and application to develop high-performance La/Ce/Y-rich permanent magnets with deliberately tailored GB phase.

Hollow Villages and Rural Restructuring in Major Rural Regions of China: A Case Study of Yucheng City, Shandong Province

The agricultural land resources of China are relatively limited because of its large population.Therefore,balancing the land use for industrialization,urbanization,and food security is a big challenge.In recent years,rural hollowing in China has resulted in numerous of abandoned rural houses,and the areas with abandoned houses need to be restored into agricultural land with effective land consolidation techniques.This study used the method of benefit-cost analysis and the data collected through field surveys conducted in Yucheng City in the northwest of Shandong Province in March 2009,to examine how hollow villages (HVs) to be created and how to solve the problem.The qualitative and quantitative analyses indicate following results.1) The situation of HVs is becoming increasingly severe under rapid industrialization and urbanization in Yucheng City.2) Poor infrastructure in rural areas and incomplete urbanization are the main factors that have led to the rural hollowing in many major rural regions of China.3) In order to resolve the problem caused by HVs and increase agricultural land,reconstructing rural communities in the countryside is necessary.4) A new mechanism in the provision of compensation funds by developed regions to the villages in less-developed regions must be established.

Time-Frequency Characteristics of the Relationships Between Tropical Indo-Pacific SSTs

In this study, several advanced analysis methods are applied to understand the relationships between the Nino-3.4 sea surface temperatures （SST） and the SSTs related to the tropical Indian Ocean Dipole （IOD）. By analyzing a long data record, the authors focus on the time-frequency characteristics of these relationships, and of the structure of IOD. They also focus on the seasonal dependence of those characteristics in both time and frequency domains. Among the Nino-3.4 SST, IOD, and SSTs over the tropical western Indian Ocean （WIO） and eastern Indian Ocean （EIO）, the WIO SST has the strongest annual and semiannual oscillations. While the Nino-3.4 SST has large inter-annual variability that is only second to its annual variability, the IOD is characterized by the largest semiannual oscillation, which is even stronger than its annual oscillation. The IOD is strongly and stably related to the EIO SST in a wide range of frequency bands and in all seasons. However, it is less significantly related to the WIO SST in the boreal winter and spring. There exists a generally weak and unstable relationship between the WIO and EIO SSTs, especially in the biennial and higher frequency bands. The relationship is especially weak in summer and fall, when IOD is apparent, but appears highly positive in winter and spring, when the IOD is unimportantly weak and even disappears. This feature reflects a caution in the definition and application of IOD. The Nino-3.4 SST has a strong positive relationship with the WIO SST in all seasons, mainly in the biennial and longer frequency bands. However, it shows no significant relationship with the EIO SST in summer and fall, and with IOD in winter and spring.

Improving the resolution of seismic traces based on the secondary time-frequency spectrum

The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.

ECCM scheme against interrupted sampling repeater jammer based on time-frequency analysis

The interrupted sampling repeater jamming（ISRJ） is an effective deception jamming method for coherent radar, especially for the wideband linear frequency modulation（LFM） radar. An electronic counter-countermeasure（ECCM） scheme is proposed to remove the ISRJ-based false targets from the pulse compression result of the de-chirping radar. Through the time-frequency（TF） analysis of the radar echo signal, it can be found that the TF characteristics of the ISRJ signal are discontinuous in the pulse duration because the ISRJ jammer needs short durations to receive the radar signal. Based on the discontinuous characteristics a particular band-pass filter can be generated by two alternative approaches to retain the true target signal and suppress the ISRJ signal. The simulation results prove the validity of the proposed ECCM scheme for the ISRJ.

Diagenesis and Restructuring Mechanism of Oil and Gas Reservoir in the Marine Carbonate Formation,Northeastern Sichuan:A Case Study of the Puguang Gas Reservoir

Based on the technology of balanced cross-section and physical simulation experiments associated with natural gas geochemical characteristic analyses, core and thin section observations, it has been proven that the Puguang gas reservoir has experienced two periods of diagenesis and restructuring since the Late Indo-Chinese epoch. One is the fluid transfer controlled by the tectonic movement and the other is geochemical reconstruction controlled by thermochemical sulfate reduction （TSR）. The middle Yanshan epoch was the main period that the Puguang gas reservoir experienced the geochemical reaction of TSR. TSR can recreate the fluid in the gas reservoir, which makes the gas drying index higher and carbon isotope heavier because C2＋ （ethane and heavy hydrocarbon） and 12C （carbon 12 isotope） is first consumed relative to CH4 and 13C （carbon 13 isotope）. However, the reciprocity between fluid regarding TSR （hydrocarbon, sulfureted hydrogen （H2S）, and water） and reservoir rock results in reservoir rock erosion and anhydrite alteration, which increases porosity in reservoir, thereby improving the petrophysical properties. Superimposed by later tectonic movement, the fluid in Puguang reservoir has twice experienced adjustment, one in the late Yanshan epoch to the early Himalayan epoch and the other time in late Himalayan epoch, after which Puguang gas reservoir is finally developed.

Time-frequency response spectrum of rotational ground motion and its application

The rotational seismic motions are estimated from one station records of the 1999 Jiji （Chi-Chi）, Taiwan, earthquake based on the theory of elastic plane wave propagation. The time-frequency response spectrum （TFRS） of the rotational motions is calculated and its characteristics are analyzed, then the TFRS is applied to analyze the damage mechanism of one twelve-storey frame concrete structure. The results show that one of the ground motion components can not reflect the characteristics of the seismic motions completely; the characteristics of each component, especially rotational motions, need to be studied. The damage line of the structure and TFRS of ground motion are important for seismic design, only the TFRS of input seismic wave is suitable, the structure design is reliable.