Paper Info
Title
Modeling mill load parameter based on LASSO using multi-scale high dimensional frequency spectra data 1
Abstract
There are complex mapping relationships among different mill load parameters and multi-scale frequency spectra of ball millu0027s 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 u0026 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.
Year
DOI
Venue
2017
10.1109/ICInfA.2017.8079032
international conference on information and automation
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
5
5
Name
Order
Citations
PageRank
Jian Tang1526148.30
Junfei Qiao221.38
zhuo liu372.52
Tianyou Chai42014175.55
Wen Yu524652.12