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Fault Classification and Diagnosis of UAV motor Based on Estimated Nonlinear Parameter of Steady-State Model

Jun-yong Lee, Won-tak Lee, Sang-ho Ko, and Hwa-suk Oh
Korea Aerospace University, Goyang, South Korea

Abstract—UAVs are now applied to various fields, from military missions to civilian applications. A malfunction in the drone’s thrust system during flights can result in collisions and damages of property or human injury. To prevent this, the tolerant control of the multicopter has been studied to stabilize attitude, but it tends to focus on short-sighted management. In this paper, we propose an overall fault diagnostic technique for the UAV motor itself. To do this, we derive a model for the UAV motor in the normal steady-state using a nonlinear equation, which is then experimentally verified with 99% accuracy. We consider bearing friction increase, phase open, propeller broken, transistor open, and back EMF signal errors for malfunction of UAV motor, and we suggest a simple fault diagnosis algorithm by an analysis of the fault characteristics. We show the effectiveness of our diagnostic technique by the experimental results of the testbed and flight model.

Index Terms—fault diagnosis, hardware-based simulation, modeling, multicopter, nonlinear equations, steady-state, UAV motor

Cite: Jun-yong Lee, Won-tak Lee, Sang-ho Ko, and Hwa-suk Oh, "Fault Classification and Diagnosis of UAV motor Based on Estimated Nonlinear Parameter of Steady-State Model," International Journal of Mechanical Engineering and Robotics Research, Vol. 10, No. 1, pp. 22-31, January 2021. DOI: 10.18178/ijmerr.10.1.22-31

​Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.