Proceedings of the 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)

The Ball Mill Load Measuring algorithm though Grinding tone signal based on GA

Authors
Yingmin Yi, Haichuan Yang, Lu Sun, Xiaoli Liu
Corresponding Author
Yingmin Yi
Available Online December 2017.
DOI
https://doi.org/10.2991/anit-17.2018.2How to use a DOI?
Keywords
Ball mill load, Grinding tone signal, Genetic algorithm, RBF neural networkBall mill load, Grinding tone signal, Genetic algorithm, RBF neural networkBall mill load, Grinding tone signal, Genetic algorithm, RBF neural networkBall mill load, Grinding tone signal, Genetic algorithm, RBF neural network
Abstract
For a high energy loss and complex system of ball mill, this paper provide a ball mill load detection method based on genetic algorithm optimizing BP neural network. The effective frequency range of mill sound signal is analyzed. The soft measurement model of mill load based on mill sound signal is built. In order to solve the problem which converge slowly and easily reach minimal value, the global optimization of GA (genetic algorithm) local optimization of BP neural network will be combined to improve the BP neural network. Compare with the detected mill load error generated from existing BP neural network and RBF neural network based on K-means. The experiments results show that the proposed algorithm has better precision.
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Proceedings
2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2017
ISBN
978-94-6252-447-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/anit-17.2018.2How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yingmin Yi
AU  - Haichuan Yang
AU  - Lu Sun
AU  - Xiaoli Liu
PY  - 2017/12
DA  - 2017/12
TI  - The Ball Mill Load Measuring algorithm though Grinding tone signal based on GA
BT  - 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/anit-17.2018.2
DO  - https://doi.org/10.2991/anit-17.2018.2
ID  - Yi2017/12
ER  -