Modeling mill load parameter based on LASSO using multi-scale high dimensional frequency spectra data1

Abstract

There are complex mapping relationships among different mill load parameters and multi-scale frequency spectra of ball mill's mechanical vibration and acoustic signals. Aim at to construct an effective and meaningful soft sensor model, how to select interesting input variables of each local-scale frequency spectrum and how to fuse these different multi-scale ones jointly, is still an un-solved open issue. A new method based on LASSO (Least Absolute Shrinkage and Selection Operator) and SEN (Selective Ensemble) algorithm for mill load parameter forecasting (MLPF) is proposed in this paper. Candidate submodels are constructed with LASSO based on each local-scale frequency spectrum, and the valued input features are selected at the same time. These ensemble sub-models are selected and combined based on SEN approach by using branch & bound (BB) and adaptive weighting fusion (AWF) algorithms. Regularization coefficients of all candidate sub-models are selected together to ensure diversities among these selected ensemble sub-models. Multi-scale frequency spectra data of mechanical vibration and acoustic signals based on a laboratory scale ball mill are used to validate the proposed method.