This paper investigates an algorithm to the robust fault detection and isolation(FDI) in robot manipulators using Neural Networks(NNs). Two Neural Networks are utilized: the first NN (NN1) is employed to reproduce the robot's dynamic behavior, while the second NN (NN2) is used to achieve the online approximation for fault detection and isolation. This approach focused on detecting changes in the robot dynamics due to faults. An online monitoring is used not only to detect faults but also to provide estimates of the fault characteristics. A computer simulation example for a two link robot manipulator shows the effectiveness of the proposed algorithm in the fault detection and isolation design process. © 2011 Springer-Verlag.
CITATION STYLE
Van, M., Kang, H. J., & Ro, Y. S. (2011). A robust fault detection and isolation scheme for robot manipulators based on neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6838 LNCS, pp. 25–32). https://doi.org/10.1007/978-3-642-24728-6_4
Mendeley helps you to discover research relevant for your work.