Abstract
Load parameters inside the ball mill have direct relationships with the optimal operation of grinding process. This paper aims to develop a selective ensemble modeling approach to estimate these parameters. At first, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) adaptively. Then, frequency spectra of these IMFs are obtained via fast Fourier transform (FFT), and a serial of kernel partial least squares (KPLS) sub-models are constructed based on these frequency spectra. At last, the ensemble models are obtained by integrating the branch and band (BB) algorithm and the information entropy-based weighting algorithm. Experimental results based on a laboratory scale ball mill indicate that the propose approach not only has better prediction accuracy, but also can interpret the vibration signal more deeply.
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Tang, J., Zhao, LJ., Long, J., Chai, Ty., Yu, W. (2012). Selective Ensemble Modeling Parameters of Mill Load Based on Shell Vibration Signal. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_55
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DOI: https://doi.org/10.1007/978-3-642-31346-2_55
Publisher Name: Springer, Berlin, Heidelberg
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