Fault Feature Enhancement and Diagnosis Method for UAV Rotor Bearings Based on Amplitude-Modulated Graph Spectrum

Abstract: With the continuous improvement of integration and multifunctionality in unmanned aerial vehicle (UAV) systems, their operational reliability is facing increasing challenges, and the failure rate has correspondingly risen. Under long-term operating conditions, motor bearings, as critical components of the propulsion system, are subjected to sustained heavy loads and thus become one of the most failure-prone components. To address these issues, this paper proposes a method for diagnosing faults in UAV rotor bearings based on an amplitude-modulated graph spectral model. First, an amplitude modulation framework using the short-time Fourier transform (STFT) is constructed, and the LSES indicator is introduced to quantitatively evaluate fault feature information under different modulation structures. Subsequently, the signal corresponding to the maximum LSES value is selected to construct a graph signal model. Finally, the last K-order spectral components in the graph frequency domain are retained for feature extraction and enhancement. Validation using both simulated signals and experimental signals of bearing inner- and outer-race faults demonstrates that the proposed method has good feasibility and effectiveness.