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作者:

Tang, Jian (Tang, Jian.) | Zhu, Hongjuan (Zhu, Hongjuan.) | Li, Dong (Li, Dong.) (学者:李冬) | Zhang, Jian (Zhang, Jian.) | Yu, Gang (Yu, Gang.)

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EI

摘要:

The mapping relationship between the mill load and the multi-component mechanical signals generated by the ball mill of the mineral grinding process is non-deterministic and complex. With the inherent filtering function of the human ear, the operating expert can effectively estimate the mill load and its internal parameters for their familiar mill in the actual industrial process. In order to obtain multiple single-mode sub-signals with physical meaning and complementary characteristic, this paper proposes a single-mode sub-signal selection method based on variational modal decomposition (VMD) and predictive performance. At first, based on prior knowledge, the value of decomposition layers required to perform VMD is determined. Then, VMD is used to decompose the original mechanical signal into multiple time-domain single-mode sub-signals with different bandwidths and time scales, and further are transformed to the frequency domain to obtain candidate single-mode sub-signal frequency spectrum. Finally, based on these candidate spectral data, a serial of candidate sub-models for mill load parameter prediction are constructed, and a series of selective ensemble models are built for obtaining reduced single-mode sub-signal frequency spectrum, The final single-mode sub-signals with the biggest complementary characteristics are selected based on the practical requirement. The effectiveness of the method is demonstrated by comparative experiment simulations based on the shell vibration signal of a laboratory-scale ball mill. © 2020 Technical Committee on Control Theory, Chinese Association of Automation.

关键词:

Ball mills Frequency domain analysis Signal processing Spectroscopy Time domain analysis

作者机构:

  • [ 1 ] [Tang, Jian]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 2 ] [Zhu, Hongjuan]Pla Navy, Beijing; 100129, China
  • [ 3 ] [Li, Dong]Joint Staff of the Central Military Commission, 55th Institute, Beijing; 100128, China
  • [ 4 ] [Zhang, Jian]Nanjing University of Information Science and Technology, College of Computer and Software, Nanjing; 210044, China
  • [ 5 ] [Yu, Gang]State Beijing Key Laboratory of Process Automation in Mining Metallurgy, Beijing; 100160, China

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ISSN: 1934-1768

年份: 2020

卷: 2020-July

页码: 5730-5735

语种: 英文

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