Name
Title | ||
|---|---|---|
Optimized ensemble modeling based on feature selection using simple sphere criterion for multi-scale mechanical frequency spectrum |
Abstract | ||
|---|---|---|
Several parameters of industrial processes are indirectly measured by multi-scale mechanical frequency spectrum. Selecting suitable mechanical sub-signals and relevant frequency spectral features for different process parameters remains an open issue. This study proposes a new optimized ensemble model based on feature selection using simple sphere criterion (SSC). Mechanical signals are adaptively decomposed and transformed into frequency spectral data with different timescales. These spectral data are fed into adaptive multi-scale spectral feature selection and modeling framework, in which local-scale frequency spectral features are adaptively selected with concurrent projection to latent structures and SSC based on unscaled data. The optimized ensemble model is constructed with selective information fusion strategy based on reduced frequency spectral data. The feature selection and model learning parameters are jointly selected. Simulation results based on the mechanical vibration and acoustic signals of an experimental laboratory-scale ball mill show the effectiveness of the proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00500-018-3373-9 | soft computing |
Keywords | Field | DocType |
Multi-scale mechanical frequency spectrum, Concurrent projection to latent structure (CPLS), Simple sphere criterion (SSC), Selective information fusion, Optimized ensemble modeling | Reduced frequency,Mathematical optimization,Ensemble forecasting,Feature selection,Computer science,Algorithm,Spectral data,Frequency spectrum,Mechanical vibration,Information fusion,Model learning | Journal |
Volume | Issue | ISSN |
23.0 | 16.0 | 1433-7479 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Tang | 1 | 526 | 148.30 |
| Jun-Fei Qiao | 2 | 798 | 74.56 |
| zhuo liu | 3 | 7 | 2.52 |
| Xiaojie Zhou | 4 | 15 | 3.04 |
| Gang Yu | 5 | 431 | 34.83 |
| Jianjun Zhao | 6 | 937 | 73.20 |