Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Modeling Load Parameters of Ball Mill in Grinding Process Based on Selective Ensemble Multisensor Information

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Jian Tang ; Unit 92941, PLA, Huludao, China ; Tianyou Chai ; Wen Yu ; Lijie Zhao

Due to complex dynamic characteristics of the ball mill system, it is difficult to measure load parameters inside the ball mill. It has been noticed that the traditional single-model and ensemble-model based soft sensor approaches demonstrate weak generalization power. Also, mill motor current, feature subsets of the shell vibration and acoustical frequency spectra contain different useful information. To achieve better solutions and overcome these problems mentioned above, a selective ensemble multisource information approach is proposed in this paper. Only the useful feature subsets of vibration and acoustical frequency spectra are portioned and selected. Some modeling techniques, such as fast Fourier transform (FFT), mutual information (MI), kernel partial least square (KPLS), brand and band (BB), and adaptive weighting fusion (AWF), are combined effectively to model the mill load parameters. The simulation is conducted using real data from a laboratory-scale ball mill. The results show that our proposed approach can effectively fusion the shell vibration, acoustical and mill motor current signals with improved model generalization.

Published in:

Automation Science and Engineering, IEEE Transactions on  (Volume:10 ,  Issue: 3 )