Abstract
This paper proposes a novel learning approach to online condition monitoring of robotic machines. The real-time learning process comprises three stages, domain knowledge defining, random learning and ordinal learning. Domain knowledge defining abstracts the model of a robotic machine; random learning and ordinal learning stages train the parameters of the abstract model with random data selection and ordinal data selection, respectively. Simulation results have proved that the pro-posed method is efficient and feasible for online fault diagnosis of robotic machines.
Original language | English |
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Pages (from-to) | 185-196 |
Number of pages | 12 |
Journal | Facta Universitatis Series Mechanics, Automatic Control and Robotics |
Volume | 7 |
Issue number | 1 |
Publication status | Published - 2008 |