Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time se...Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.展开更多
To solve the problem of the flashover forecasting of contaminated or polluted insulator,a flashover forecasting model of contaminated insulators based on nonlinear time series analysis is proposed in the paper.The ESD...To solve the problem of the flashover forecasting of contaminated or polluted insulator,a flashover forecasting model of contaminated insulators based on nonlinear time series analysis is proposed in the paper.The ESDD is the key of flashover on polluted insulator.The ESDD value of insulator can be forecasted by the method of nonlinear time series analysis of the ESDD time series and a forecasting model of polluted insulator flashover is proposed in the paper.The forecasting model consists of two artificial neural networks that reflect relationship of environment,ESDD and flashover probability.The first is used to estimate the ESDD time series of insulator and the second is employed to calculate the probability of the flashover.A series of artificial pollution tests show that the results of the forecasting model is acceptable.展开更多
The paper builds up the forecasting model of air temperature according to the data (1994～1998) of Fu Jin area.At the same time,the writer inquires into the relation of water requirement of well irrigation rice (ET) a...The paper builds up the forecasting model of air temperature according to the data (1994～1998) of Fu Jin area.At the same time,the writer inquires into the relation of water requirement of well irrigation rice (ET) and average air temperature (T).Furthermore,the rice irrigation water requirement (ET) of Fu Jin area has been forecast in 1999.Thus,we can apply the model in irrigation management.展开更多
In order to improve the screening efficiency of vibrating screen and make vibration process smooth,a new type of magnetorheological (MR) damper was proposed. The signals of displacement in the vibration process during...In order to improve the screening efficiency of vibrating screen and make vibration process smooth,a new type of magnetorheological (MR) damper was proposed. The signals of displacement in the vibration process during the test were collected. The trispectrum model of autoregressive (AR) time series was built and the correlation dimension was used to quantify the fractal characteristics during the vibration process. The result shows that,in different working conditions,trispectrum slices are applied to obtaining the information of non-Gaussian,nonlinear amplitude?frequency characteristics of the signal. Besides,there is correlation between the correlation dimension of vibration signal and trispectrum slices,which is very important to select the optimum working parameters of the MR damper and vibrating screen. And in the experimental conditions,it is found that when the working current of MR damper is 2 A and the rotation speed of vibration motor is 800 r/min,the vibration screen reaches its maximum screening efficiency.展开更多
Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
Vibration equations of time-varying system are transformed to the form which is suitable to precise integration algorithm.Precision analysis and computation efficiency of new algorithm are implemented.The following co...Vibration equations of time-varying system are transformed to the form which is suitable to precise integration algorithm.Precision analysis and computation efficiency of new algorithm are implemented.The following conclusions can be got.Choosing matrixes M,G and K is certainly flexible.We can place left side of nonlinear terms of vibration equations of time-varying system into right side of equations in precise integration algorithms.The key of transformation from vibration equations of time-varying system to first order differential equations is to form matrix H,which should be assured to be nonsingular.With suitable disposal,precision and computation efficiency of precise integration algorithms are greatly larger than those of general methods.展开更多
Higher order spectral analysis can be used to identify nonlinearities in the complex dynamical systems.This proposal shows that the contributions of the bispectrum,trispectrum,reconstructed bispectrum and reconstructe...Higher order spectral analysis can be used to identify nonlinearities in the complex dynamical systems.This proposal shows that the contributions of the bispectrum,trispectrum,reconstructed bispectrum and reconstructed power spectrum in terms of the system frequency response function and elementary physical properties of the MR damping system.Subsequent estimates of the HOS based on the output stochastic oscillating signals appear distinct variation.An experimental platform for MR vibrating semi-active control is built,proper simplifications are presented,an AR(10) model is established with colored noises from the output signals.Comparison between power spectrum from second order moment function and bispectrum,trispectrum are taken.The later gives an indication of the correlation between the phases of different frequency components.Since time series model is a parametric model,the reconstructed bispectrum and power spectrum are smooth.It is demonstrated that the higher order spectra are effectively for recognition and description of nonlinear systems.展开更多
基金Projects(61271321,61573253,61401303)supported by the National Natural Science Foundation of ChinaProject(14ZCZDSF00025)supported by Tianjin Key Technology Research and Development Program,China+1 种基金Project(13JCYBJC17500)supported by Tianjin Natural Science Foundation,ChinaProject(20120032110068)supported by Doctoral Fund of Ministry of Education of China
文摘Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.
基金Project Supported by Cultiration Found of the Key Scientific and Technical Innovation Project,Ministry of Education of China(707018)
文摘To solve the problem of the flashover forecasting of contaminated or polluted insulator,a flashover forecasting model of contaminated insulators based on nonlinear time series analysis is proposed in the paper.The ESDD is the key of flashover on polluted insulator.The ESDD value of insulator can be forecasted by the method of nonlinear time series analysis of the ESDD time series and a forecasting model of polluted insulator flashover is proposed in the paper.The forecasting model consists of two artificial neural networks that reflect relationship of environment,ESDD and flashover probability.The first is used to estimate the ESDD time series of insulator and the second is employed to calculate the probability of the flashover.A series of artificial pollution tests show that the results of the forecasting model is acceptable.
基金Funditem:China Postdoctoral Science Fund(2 0 0 0 ).The Youth Fund of Sichuan U niversity.(43 2 0 2 8)
文摘The paper builds up the forecasting model of air temperature according to the data (1994～1998) of Fu Jin area.At the same time,the writer inquires into the relation of water requirement of well irrigation rice (ET) and average air temperature (T).Furthermore,the rice irrigation water requirement (ET) of Fu Jin area has been forecast in 1999.Thus,we can apply the model in irrigation management.
基金Project(50975098) supported by the National Natural Science Foundation of ChinaProject(2008HZ0002-1) supported by the Major Scientific and Technological Program of Fujian Province,China
文摘In order to improve the screening efficiency of vibrating screen and make vibration process smooth,a new type of magnetorheological (MR) damper was proposed. The signals of displacement in the vibration process during the test were collected. The trispectrum model of autoregressive (AR) time series was built and the correlation dimension was used to quantify the fractal characteristics during the vibration process. The result shows that,in different working conditions,trispectrum slices are applied to obtaining the information of non-Gaussian,nonlinear amplitude?frequency characteristics of the signal. Besides,there is correlation between the correlation dimension of vibration signal and trispectrum slices,which is very important to select the optimum working parameters of the MR damper and vibrating screen. And in the experimental conditions,it is found that when the working current of MR damper is 2 A and the rotation speed of vibration motor is 800 r/min,the vibration screen reaches its maximum screening efficiency.
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
基金Project(50078006) supported by the National Natural Science Foundation of China
文摘Vibration equations of time-varying system are transformed to the form which is suitable to precise integration algorithm.Precision analysis and computation efficiency of new algorithm are implemented.The following conclusions can be got.Choosing matrixes M,G and K is certainly flexible.We can place left side of nonlinear terms of vibration equations of time-varying system into right side of equations in precise integration algorithms.The key of transformation from vibration equations of time-varying system to first order differential equations is to form matrix H,which should be assured to be nonsingular.With suitable disposal,precision and computation efficiency of precise integration algorithms are greatly larger than those of general methods.
基金Project(A0610020) supported by the Natural Science of Fujian Province of China
文摘Higher order spectral analysis can be used to identify nonlinearities in the complex dynamical systems.This proposal shows that the contributions of the bispectrum,trispectrum,reconstructed bispectrum and reconstructed power spectrum in terms of the system frequency response function and elementary physical properties of the MR damping system.Subsequent estimates of the HOS based on the output stochastic oscillating signals appear distinct variation.An experimental platform for MR vibrating semi-active control is built,proper simplifications are presented,an AR(10) model is established with colored noises from the output signals.Comparison between power spectrum from second order moment function and bispectrum,trispectrum are taken.The later gives an indication of the correlation between the phases of different frequency components.Since time series model is a parametric model,the reconstructed bispectrum and power spectrum are smooth.It is demonstrated that the higher order spectra are effectively for recognition and description of nonlinear systems.
