Analysis of the vibration characteristics of an experimental mechanical system using neural networks

This paper presents an investigation on the vibration analysis of a gearing mechanism using neural network predictors. The experimental system is positioned on a working table with changeable legs. The legs have different shapes such as L, H and O shapes, for finding the exact leg profiles for the experimental system. Two types of neural networks are used to predict vibrations of the system for different leg profiles. The results of two approaches indicate that the proposed neural network with Levenberg-Marquardt learning algorithm has a superior performance to predict vibration parameters of the system.This paper presents an investigation on gearing mechanism’s vibration analysis using neural network predictors. The experimental system is positioned on a working table with changeable legs. The legs have different shapes such as L, H and O shapes for finding the exact leg profiles for the experimental system. Two types of neural networks are used to predict vibrations of the system for different leg profiles. The results of two approaches indicate that the proposed neural network with Levenberg-Marquardt (LM) learning algorithm has a superior performance to predict vibration parameters of the system