Fault detection on robot manipulators using artificial neural networks

Nowadays gas welding applications on vehicle s parts with robot manipulators have Increased in automobile industry Therefore the speed of end-effectors of robot manipulator is affected on each joint during the welding process with complex trajectory For that reason It is necessary to analyze the noise and vibration of robot s joints for predicting faults This paper presents an experimental investigation on a robot manipulator using neural network for analyzing the vibration condition on joints Firstly robot manipulator s joints are tested with prescribed of trajectory end-effectors for the different joints speeds Furthermore noise and vibration of each joint are measured And then the related parameters are tested with neural network predictor to predict servicing period In order to find robust and adaptive neural network structure two types of neural predictors are employed in this investigation The results of two approaches improved that an RBNN type can be employed to predict the vibrations on industrial robots Crown Copyright (C) 2010 Published by Elsevier Ltd All rights reserved