Papers by Keyword: Time-Series

Abstract: The chaotic time series phase space reconstruction theory based in this paper. First, the appropriate embedding dimension and delay time are selected by minimum entropy rate. Followed the chaotic behavior are analyzed by the use of the Poincare section map and Power spectrum of time series from the qualitative point of view. Based on NLSR LLE the quantitative study of the chaotic time series characteristics indicators is proposed. Finally, the gas emission workface of Hebi 10th Mine Coal is studied. The several analytical results of the above methods show that: the gas emission time-series data of this workface has chaotic characteristics.

Abstract: In this paper, the development scenarios of electricity consumption of city residents living were analyzed by using the prediction method of time-series smoothing. The simulation calculation to the future development scenarios of electricity consumption forecasts the electricity consumption values of residential building in this case area from 2010 to 2050. The conclusion, that controlling living area per person is most effective measure, has reference significance for the future residential building energy efficiency work.

Abstract: The friction torque of rolling bearings belongs to an information poor system with unknown probability distributions and trends. This counteracts dynamical assessment for the characteristics of the rolling bearing friction torque as a time series. For this reason, the chaos theory is employed to recover the original dynamic characteristics of a friction torque time series by means of the phase space reconstruction theory. The dynamical Bayesian probability density function of the characteristic parameters of the friction torque is constructed by the information poor theory based on the phase space. The method for point estimation, interval estimation, and trend estimation of the characteristic parameters is proposed in this paper. The investigation shows that the error between the calculated result and the experimental result is very small.

Abstract: The standard uncertainty in the measurement theory is applied to evaluate the change of the rolling bearing vibration acceleration generated by the failure on the surface of the ring raceway. The time series are obtained via the experimental investigation on the vibrational acceleration of the rolling bearings with different failure diameters. And the result shows that the standard uncertainty of the vibrational acceleration increases nonlinearly with the failure diameter, revealing a new characteristic of the variation of the rolling bearing failure process. It follows that for a rolling bearing in running, the failure process can be described by the standard uncertainty of its vibration acceleration, laying a foundation for failure warning of a rolling bearing.

Abstract: With the PV power system capacity continues to expand, PV power generation forecasting techniques can reduce the PV system output power of randomness, it has great impact on power systems. This paper presents a method based on ARMA time series power prediction model. With historical electricity data and meteorological factors, the model gets test and evaluation by Eviews software. Results indicated that the prediction model has high accuracy, it can solve the shortcomings of PV randomness and also can improve the ability of the stable operation of the system.

Abstract: Based on the fundamental principles of the wavelet analysis combining with BP neural network, the paper can obtain the minimum embedding dimension and delay time. According to the chaos theory, the phase space of the magnitude time series can be reconstructed by Takens theorem. The paper uses wavelet neural network to train and test the nonlinear magnitude time series in the reconstructed phase space. The simulation results show that the predictive effect of the magnitude time series is remarkable and the predictive performance of single-step prediction is superior to that of multi-step prediction.

Abstract: In accelerated degradation test, it is essential to establish a suitable degradation path model of the component. In order to predict the life accurately, it is critical to determine the sensitive parameters. A degradation path modeling method based on time series analysis was proposed in this paper. The sensitive parameters were determined by time series stationary test, and a time series ARIMA model was established for the degradation of sensitive parameters. Taking TL431 as an example, the accelerated degradation tests were carried out to investigate the degradation of its performance parameters, and a degradation path model was established for the parameters. The results indicate that the proposed method is feasible for accelerated degradation test.

Abstract: When the AR model is used to identify the structural damage, one problem is often met, that is the method can only make a decision whether the structure is damaged, however, the damage location can not be identified exactly. A structural damage localization method based on AR model in combination with BP neural network is proposed in this paper. The AR time series models are used to describe the acceleration responses. The changes of the first 3-order AR model parameters are extracted and composed as damage characteristic vectors which are put into BP neural network to identify the damage location. The effectiveness of the method is validated by the results of numerical simulation and experiment for a four-layer offshore platform. Only the acceleration responses can be used adequately to localize the structural damage, without the usage of modal parameter and excitation force. Thus the dependence on the modal parameter and excitation can be avoided in this method.

Abstract: The corrosion of LY12CZ aluminum alloy in aircraft under service environment is regarded as a stochastic process and the time series theory is used to analyze and to predict the corrosion depth of LY12CZ under airport environment by means of ARIMA(3,1,1)model.The application result show that the ARIMAmodel can predict the value and propagation trend of corrosion depth realistically and effectively,demonstrating the expedient and easy application of time series theory and method.

Abstract: The bridge health monitoring system can collect large amounts of data, but it lacks the trend analysis of monitoring data. This article introduced the method of Time series analysis into the analysis of bridge monitoring data, and adopted ARIMA model in time series analysis of monitoring data, used the least square method for parameter estimation, established the prediction model for bridge deflection, and conducted the goodness of fit test. Take the actual bridge monitoring data as an example, it was demonstrated that the method is feasible in the prediction of bridge condition trend.