Journal of Northeastern University Natural Science ›› 2015, Vol. 36 ›› Issue (3): 305-308.DOI: 10.12068/j.issn.1005-3026.2015.03.001

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Multi-scale Shell Vibration Frequency Spectrum Analysis and Modeling Approach of Ball Mill

LIU Zhuo1, CHAI Tian-you1, TANG Jian2   

  1. 1. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China; 2. Unit 92941, PLA, Huludao 125001, China.
  • Received:2014-01-07 Revised:2014-01-07 Online:2015-03-15 Published:2014-11-07
  • Contact: LIU Zhuo
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Abstract: Single-scale shell vibration frequency spectrum cannot reflect grinding mechanism of ball mill and analyze the composition of shell vibration signal. The empirical mode decomposition (EMD) based on soft sensor methods in present literature have poor prediction accuracy. Aiming at these problems, a new multi-scale shell vibration frequency spectrum analysis and modeling approach based on the partial least squares (PLS) algorithm was proposed. Firstly, the shell vibration acceleration signal was decomposed into different time-scale intrinsic mode functions (IMF) adaptively. Then, multi-scale frequency spectrum was obtained by using fast Fourier transform to different IMFs. Finally, different scale frequency spectrum was analyzed and selected by the latent variables contribution of the PLS algorithm. In addition, mill load parameters’ soft sensor model was constructed by fusing these selected multi-scale frequency spectrum. Simulation results based on experimental data of the laboratory ball mill validate the effectiveness of the proposed method.

Key words: multi-scale frequency spectrum, empirical mode decomposition (EMD), partial least squares(PLS) algorithm, shell vibration, mill load

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