Papers by Keyword: Time Series Model

Abstract: The prediction of tool wear can help understand the influence of tool wear on the machining process and result, and change or grind the worn tool in time. The two-dimensional ultrasonic vibration turning method can reduce the crack of tool and decrease the negative effect on processing thus extends the tool life. In this paper, two-dimensional ultrasonic cutting theory was applied to the precision machining of tungsten carbide. With self-developed two-dimensional ultrasonic cutting device, series of cutting experiments were carried out. During cutting process, the flank wear under different cutting length was observed; flank wear situations were compared with those in traditional cutting. In order to predict the tool wear and thus heighten the machining precision, a tool wear prediction model based on time series analysis method was built in the paper. The research results show the built AR (9) time series model can predict the flank wear condition with high precision.

Abstract: Time series analysis has been extensively used in many fields, such as system identification, modeling and data predication, and played an important role in system design, planning and performance analysis. The focus of time series application study is how to improve the accuracy and computation speed of the parameter estimation. Many researchers have carried out system modeling study by applying time series analysis and have gained their research results. The traditional methods such as maximum likelihood estimation, moment estimate and least square estimate which exit the defect of low precision, poor convergence and parameter estimation white noises coupling, are mostly utilized in parameter estimation for model. Taking this as basis the data forecasting and anomaly detection are conducted, which is hard to ensure the system’s stability. Different from the traditional algorithm, this paper proposes a new weighted iterative stage parameter estimation algorithm which avoids the coupling with white noise estimation of ARMA model parameter and improves the accuracy of parameter estimation. In theory, this algorithm tends to provide a good convergence performance. The experimental results based on ARIMA model show that the algorithm can improve the accuracy of parameter estimation and provide a good convergence performance.

Abstract: With the development of power industry and the growth of high-voltage power equipments in electric power company, the supply and management of spare parts are becoming more complexity and onerous. This investigation proposed a hybrid method to effectively predict the requirements of electric spare parts utilizing fuzzy logic and ARIMA so as to provide as a reference of spare parts control. The forecasting methods are tested in an empirical, comparative study for an electric power company of China. The results show that the approach is one of the most accurate methods.

Abstract: The algorithms and the applications of UKF filtering of UAV MEMS Gyro based on time-series model are presented in this paper. First Gyro output signals are preprocessed and modeled by time-series analysis theory, and then use UKF filtering method to compensating error based on the time-series model. Examples with actual experiment demonstrate that the method has apparent superiority. The simulation result shows that, both in static and dynamic cases, after eliminate the precision error MEMS gyro accuracy can achieve the miniature UAV standards.

Abstract: Block forming machine, as a kind of automatic equipments, can quickly compact blocks. Higher-order spectrum analysis emerges as a new effective method in signal processing, which can describe nonlinear coupling, restrain Gaussian noise and reserve phase components. In the paper, a hydraulic exciter applying to block forming machine will be introduced. Then block forming machine’s random vibration signals during the compacting process would be collected, in order to make use of the sample data to build up a time series autoregressive model and bispectrum of three-order accumulation, to analyze AR bispectrum characteristics of the machine’s vibrate signals under different work conditions.

Abstract: The stochastic excitation power spectral density (PSD) model and ARMA time series model are established based on the stochastic rolling force acquisition data, which is processed into stationary, normal and zero mean from a aluminum hot strip tandem mill. Characteristics of rolling force ARMA time series models are discussed by means of random process theory. The rolling forces PSD function of facilitating engineering application is obtained by utilizing Levenberg-Marquardt combined with generalized global planning algorithm, and the stochastic excitation model is established. It provides the basis for the prediction of rolling force.

Abstract: Using time series model, isometric transformation time series model and ARTAFIT model, we deal with acoustic signal, obtaining different sets of parameters according to different acoustic signals. We use support vector machine (SVM) to recognize different acoustic signals by analyzing different sets of parameters. When the parameter set is too large, we should first reduce order making use of principal component analysis (PCA), then we can recognize them using support vector machine. In the end, we give a case study, which indicate the results of applying our models are satisfactory.

Abstract: It is important to improve the reliability of the eddy current non-destructive testing. It is generally difficult to reduce the probability of non-detection and the number of false alarms same time. Model Based Measurement (MBM), including estimation of state, failure diagnosis and trend analysis, has excellent results on general estimation. Actual results of MBM vary with the application domain, mathematical model and data processing. The time series model is a description of system in time domain based on equivalent output. The Kalman filter is an efficient method for suppressing the disturbance and improving the state estimation. A new method, called as eddy current testing (ECT) using a model based measurement, is presented. Two time series models are used for the normal status and the abnormal status in ECT. The Kalman filter on these models is applied to give useful information for decision making. Results of the experiment on aluminum plates demonstrate that this method is useful to improve the detection reliability.

Abstract: This paper studies the NC grinding process for Isometric Polygonal Profiles (IPP) with self-made machine tools accessories, and establishes the time series model of the grinding process by means of measuring and analyzing the vibratory signal in the grinding process. The grinding experiment indicates that the surface formative motions of IPP can be achieved with the step-motor-driven accessories and it is reasonable to design the grinding machine for IPP according to the principle. This method solves the technical problems in IPP NC processing and can act as an important elicitation and illustration for NC processing other polygonal profiles.