Abstract
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.
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Van, M., Kang, HJ., Ro, YS. (2011). A Robust Fault Detection and Isolation Scheme for Robot Manipulators Based on Neural Networks. In: Huang, DS., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing. ICIC 2011. Lecture Notes in Computer Science, vol 6838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24728-6_4
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DOI: https://doi.org/10.1007/978-3-642-24728-6_4
Publisher Name: Springer, Berlin, Heidelberg
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