Trispectrum and correlation dimension analysis of magnetorheological damper in vibration screen

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.

[1]  Anirban Chaudhuri,et al.  A magnetorheological actuation system: test and model , 2008 .

[2]  Diana Broboana,et al.  Rheological characterization of complex fluids in electro-magnetic fields , 2008 .

[3]  Sang Jo Lee,et al.  A behavioral model of axisymmetrically configured magnetorheological fluid using Lekner summation , 2009 .

[4]  D. Yantek,et al.  Noise And Vibration Reduction Of A Vibrating Screen , 1900 .

[5]  Pinqi Xia,et al.  An inverse model of MR damper using optimal neural network and system identification , 2003 .

[6]  Bing-san Chen,et al.  Autoregressive trispectrum and its slices analysis of magnetorheological damping device , 2008 .

[7]  I. A. El-Sonbaty,et al.  Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach , 2008 .

[8]  S. Momani,et al.  Numerical methods for nonlinear partial differential equations of fractional order , 2008 .

[9]  William Kordonski,et al.  Magnetorheological (MR) Jet Finishing Technology , 2007 .

[10]  Y. Ni,et al.  Semi-active optimal control of linearized systems with multi-degree of freedom and application , 2005 .

[11]  S. Sivaloganathan,et al.  The constitutive properties of the brain paraenchyma Part 2. Fractional derivative approach. , 2006, Medical engineering & physics.

[12]  S. X. Dou,et al.  Coexistence of ferromagnetism and cluster glass state in superconducting ferromagnet RuSr2Eu1.5Ce0.5Cu2O10−δ , 2009 .

[13]  P. N. Roschke,et al.  Fuzzy modeling of a magnetorheological damper using ANFIS , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).