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Neural Computing and Applications
Published in: Neural Computing and Applications 5/2020

19-10-2019 | Original Article

Monitoring the fill level of a ball mill using vibration sensing and artificial neural network

Authors: Dilip Kumar Nayak, Debi Prasad Das, Santosh Kumar Behera, Sarada Prasad Das

Published in: Neural Computing and Applications | Issue 5/2020

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Abstract

Ball mills are extensively used in the size reduction process of different ores and minerals. The fill level inside a ball mill is a crucial parameter which needs to be monitored regularly for optimal operation of the ball mill. In this paper, a vibration monitoring-based method is proposed and tested for estimating the fill level inside a laboratory-scale ball mill. A vibration signal is captured from the base of a laboratory-scale ball mill by using a ± 5 g accelerometer. Features are extracted from the vibration signal by using different transforms such as fast Fourier transform, discrete wavelet transform, wavelet packet decomposition, and empirical mode decomposition. These features are given as input to an artificial neural network which is used to predict the percentage fill level inside the ball mill. In this paper, the predicted fill level obtained by using different features are compared. It is found that the predicted fill level due to features obtained after fast Fourier transform outperforms other transforms.
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Metadata
Title
Monitoring the fill level of a ball mill using vibration sensing and artificial neural network
Authors
Dilip Kumar Nayak
Debi Prasad Das
Santosh Kumar Behera
Sarada Prasad Das
Publication date
19-10-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 5/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04555-5

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