Milos Milovancevic, Vlastimir Nikolic, Nenad T. Pavlovic, Aleksandar Veg and Sanjin Troha
The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any…
Abstract
Purpose
The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any system to ensure safe operations. Improvement of control strategies is crucial for the vibration monitoring.
Design/methodology/approach
As predictive control is one of the options for the vibration monitoring in this paper, the predictive model for vibration monitoring was created.
Findings
Although the achieved prediction results were acceptable, there is need for more work to apply and test these results in real environment.
Originality/value
Artificial neural network (ANN) was implemented as the predictive model while extreme learning machine (ELM) and back propagation (BP) learning schemes were used as training algorithms for the ANN. BP learning algorithm minimizes the error function by using the gradient descent method. ELM training algorithm is based on selecting of the input weights randomly of the ANN network and the output weight of the network are determined analytically.