Detecção e diagnóstico de falhas em robôs manipuladores via redes neurais artificiais.

Abstract

In this work, a new approach for fault detection and diagnosis in robotic manipulators is presented. A faulty robot could cause serious damages and put in risk the people involved. Usually, researchers have proposed fault detection and diagnosis schemes based on the mathematical model of the system. However, modeling errors could obscure the fault effects and could be a false alarm source. In this work, two artificial neural networks are employed in a fault detection and diagnosis system to robotic manipulators. A Multilayer Perceptron trained with Backpropagation algorithm is employed to reproduce the robotic manipulator dynamical behavior. The Perceptron outputs are compared with the real measurements, generating the residual vector. A Radial Basis Function Network is utilized to classify the residual vector, generating the fault isolation. Four different algorithms have been employed to train this Network. The first utilizes regularization to reduce the flexibility of the model.