Abstract
In order to improve the reliability of robotic systems, various fault detection and isolation (FDI) algorithms have been proposed. However, most of these algorithms are model-based and thus, an accurate model of the robot is required although it is hard to obtain and often time-varying. Acceleration estimation is an additional challenge in dynamic model-based algorithms as it is hard to measure accurately in practice. In this study, a neural network based fault detection algorithm that does not require the use of physical robot model and acceleration is proposed. By utilizing neural network, the fault torque can be estimated, which allows effective fault detection and diagnosis. The feasibility of the proposed fault detection algorithm is validated through various simulations and experiments.
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