D. Maincer1,*, Y. Benmahamed2, M. Mansour1, Mosleh Alharthi3, Sherif S. M. Ghonein3
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1957-1969, 2023, DOI:10.32604/iasc.2023.029210
Abstract In this paper, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) based methods are to be applied on fault diagnosis in a robot manipulator. A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work. For both classifiers, the torque, the position and the speed of the manipulator have been employed as the input vector. However, it is to mention that a large database is needed and used for the training and testing phases. The SVM method used in this paper is based on the Gaussian… More >
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