Fault Diagnosis of UAV Rotor Systems Based on Drone Nest Vibration Analysis

As unmanned aerial vehicles (UAVs) are increasingly deployed in various fields, their flight safety has become a critical issue. However, limited onboard sensing and computing resources make it difficult to perform intelligent fault monitoring and diagnosis directly on UAVs. To explore an offboard alternative, this paper investigates a drone nest vibration analysis based fault diagnosis framework for a multirotor UAV rotor system using vibration signals measured from a laboratory-scale simulated drone nest. A simplified coupled dynamic model of the UAV–drone nest system is established to analyze the transmission mechanism of rotor fault-induced vibration and to explain the observability of fault-related frequency components under the tested configuration. Considering the weak and attenuated characteristics of the nest-side vibration signals, a multi-domain feature fusion and multi-task learning network is developed to integrate time-domain, frequency-domain, and envelope-spectrum information while jointly learning fault type and rotational speed. Comparative experiments on the constructed quadrotor–drone nest test platform are conducted to validate the feasibility and effectiveness of the proposed method under the tested operating conditions.