A Comparative Study That Measures Ball Mill Load Parameters Through Different Single-Scale and Multiscale Frequency Spectra-Based Approaches

Abstract

Data-driven modeling based on the shell vibration and acoustic signals of ball mills is normally applied to overcome the subjective errors of human inference. Many previously proposed selective ensemble (SEN) modeling approaches are based on “the manipulation of input features” from the multiinformation fusion perspective, which cannot selectively and jointly fuse the information hidden in multiscale spectral features and under several operating conditions (training samples). Therefore, this study suggests a new soft measuring procedure based on ensemble empirical mode decomposition (EEMD) and SEN. An improved kernel partial least-squares algorithm for SEN that is based on “subsample training samples” is utilized to construct a soft measuring model with the selected features and training samples. This study compares such data-driven soft measuring methods. The comparative results of bootstrap-based prediction performance estimation show that different methods have specific advantages in terms of simplicity, prediction accuracy, and interpretability. The industrial application of the EEMD-SEN method is discussed in this paper, and a new virtual sample generation method is proposed to address the modeling problem based on small sample spectral data.