Name
Abstract | ||
|---|---|---|
Load parameters inside the ball mill have direct relationships with the optimal operation of grinding process. This paper aims to develop a selective ensemble modeling approach to estimate these parameters. At first, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) adaptively. Then, frequency spectra of these IMFs are obtained via fast Fourier transform (FFT), and a serial of kernel partial least squares (KPLS) sub-models are constructed based on these frequency spectra. At last, the ensemble models are obtained by integrating the branch and band (BB) algorithm and the information entropy-based weighting algorithm. Experimental results based on a laboratory scale ball mill indicate that the propose approach not only has better prediction accuracy, but also can interpret the vibration signal more deeply. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31346-2_55 | ISNN (1) |
Keywords | DocType | Citations |
empirical mode decomposition,ball mill,intrinsic mode function,shell vibration signal,laboratory scale ball mill,vibration signal,original vibration signal,selective ensemble modeling approach,information entropy-based weighting algorithm,selective ensemble modeling parameter,mill load,frequency spectrum,ensemble model | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Tang | 1 | 526 | 148.30 |
| Lijie Zhao | 2 | 41 | 9.72 |
| Jia Long | 3 | 0 | 0.34 |
| Tianyou Chai | 4 | 2014 | 175.55 |
| Wen Yu | 5 | 246 | 52.12 |