Fault diagnosis in robotic manipulators using artificial neural networks and fuzzy logic

Author

Khireddine, M.S. ; Chafaa, K. ; Slimane, N. ; Boutarfa, A.

Author_Institution

Electron. Dept., Batna Univ., Batna, Algeria

fYear

2014

fDate

17-19 Jan. 2014

Firstpage

1

Lastpage

6

Abstract

Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper presents a scheme for fault detection and isolation (FDI) via artificial neural networks and fuzzy logic. It deals with sensors and actuator fault of a three links scara robot. The proposed FDI approach is implemented on Matlab/Simulink software and tested under several types of faults. The obtained results improving the importance of this method. Then, the actuator and sensor fault are detected and isolated successfully.

Keywords

fault diagnosis; fuzzy logic; manipulators; neural nets; FDI; Matlab/Simulink software; actuator fault; artificial neural networks; computational intelligence; fault detection and isolation; fault diagnosis methods; fuzzy logic; robotic manipulators; scara robot; sensor fault; Fault diagnosis; Joints; Manipulator dynamics; Mathematical model; Vectors; Artificial Neural network; Fault Diagnosis; Fuzzy logic; robotic manipulator;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Applications and Information Systems (WCCAIS), 2014 World Congress on

Conference_Location

Hammamet

Print_ISBN

978-1-4799-3350-1

Type

conf

DOI

10.1109/WCCAIS.2014.6916571

Filename

6916571