Abstract
This work proposes a simple, low-cost and effective scheme for the detection of the most common faults in drone actuators which are, usually, permanent magnet synchronous motors (PMSM). The scheme is based on a modelling stage which only requires the current measurements from a faultless motor. From this, a simplified transfer function of the motor is derived. Then, the output of this model and a healthy motor are used as arguments of simple tests to detect the occurrence of a set of characterized faults in the target motor. The setup of the scheme and the development of the tests are straightforward. The faults considered in this work are inter-turn short-circuit, changes in friction constant and flying off propeller and other less common faults. Experimental results show that these faults are accurately detected and characterized by the proposed scheme, opening doors to further work on predictive maintenance and drone adaptive or re-configurable controllers.
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This research is funded by National Council of Science and Technology (CONACyT) of México under grants 261774 and 227601.
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Jouhet, G., González-Jiménez, L.E., Meza-Aguilar, M.A., Mayorga-Macías, W.A., Luque-Vega, L.F. (2020). Model-Based Fault Detection of Permanent Magnet Synchronous Motors of Drones Using Current Sensors. In: Ghommam, J., Derbel, N., Zhu, Q. (eds) New Trends in Robot Control. Studies in Systems, Decision and Control, vol 270. Springer, Singapore. https://doi.org/10.1007/978-981-15-1819-5_15
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DOI: https://doi.org/10.1007/978-981-15-1819-5_15
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