Sanjin Troha

AN APPLICATION OF MULTICRITERIA OPTIMIZATION TO THE TWO-CARRIER TWO-SPEED PLANETARY GEAR TRAINS

The objective of this study is the application of multi-criteria optimization to the two-carrier two-speed planetary gear trains. In order to determine mathematical model of multi-criteria optimization, variables, objective functions and conditions should be determined. The subject of the paper is two-carrier two-speed planetary gears with brakes on single shafts. Apart from the determination of the set of the Pareto optimal solutions, the weighted coefficient method for choosing an optimal solution from this set is also included in the mathematical model.

Vibration prediction of pellet mills power transmission by artificial neural network

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.