Study on Fault Diagnosis of Rolling Mill Main Transmission System Based on EMD-AR Model and Correlation Dimension
In order to improve the fault diagnosis accuracy of rolling mill main transmission system, a fault feature extraction method based on EMD (Empirical Mode Decomposition)-AR model and Correlation Dimension is proposed. In the proposed method, EMD is used to decompose the vibration signal of complex machine into several intrinsic mode functions (IMFs), then the AR models of some IMF components which contain main fault information are constructed respectively. Finally, the correlation dimensions of auto-regressive parameters in AR models are calculated. Analysis of the experimental results shows that this method not only can reflect the state changes of dynamic system profoundly and detailedly, but also can realize the separation of state features, thus it may judge the fault conditions of rolling mill main transmission system effectively.
KeywordsEmpirical mode decomposition AR model Correlation dimension Rolling mill main transmission system Fault diagnosis
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- 1.Yu, W.K., Liu, B.: Processing’s method of non-stationary transient impact torsional vibration signal for Rolling mill. Chinese Journal of Scientific Instrument 26(8), 504–505 (2005)Google Scholar
- 3.Zhao, H.: Application research of correlation dimension in the machinery equipment fault diagnosis. China Safety Science Journal 16(3), 129–134 (2006)Google Scholar
- 5.Dai, G.P., Liu, B.: Instantaneous parameters extraction based on wavelet denoising and EMD. Acta Metrologica Sinica 28(2), 158–161 (2007)Google Scholar
- 6.Liu, B., Dai, G.P.: Adaptive wavelet thresholding denoising algorithm based on white noise detection and 3σ rule. Chinese Journal of Sensors and Actuators 18(3), 473–477 (2005)Google Scholar
- 7.Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proc.R Soc.Lond.A (1998)Google Scholar