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Ensemble Empirical Mode Decomposition and Feature Selection of Vibration Signals of the Ball Mill

Lijie Zhao, Xue Feng, Jingyu Li, Decheng Yuan

Abstract


Nonlinear and non-stationary shell vibration signals of the ball mill contain abundant operation information of the ball mill. Ensemble empirical mode decomposition (EEMD) and interval partial least squares (iPLS) are used to analyze shell vibration signal and estimate mill load parameters of ball mill. The shell vibration signal is decomposed into several intrinsic mode functions (IMFs) adaptively based on EEMD method. The frequency spectrum feature for each IMF is selected by using interval partial least square (iPLS) algorithm. Ball mill load models are built on the spectrum feature of several IMFs. Experimental results show that the proposed method can extract features of shell vibration signal and estimate mill load effectively.

Keywords


Ball mill load, feature selection, ensemble empirical mode decomposition, partial least squares, interval partial least squares.

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