Abstract
Purpose: The harsh operating conditions of unmanned aerial vehicles cause crack propagation in the gear teeth in the actuator, which eventually leads to breakdown. For the prevention of service delays or failure because of crack propagation, a diagnostic process based on the built-in and add-on sensors’ signals is proposed for the planetary gearbox of unmanned aerial vehicles.BRMethods: A planetary gearbox test rig is constructed, and the motor current, the position and vibration signals are acquired for the normal and crack-induced states. Features representing the health state are extracted and selected by using a suitable performance measure. Subsequently, the K-Nearest Neighbor algorithm is applied to the selected features, from which the classifications of the normal and fault states are performed.BRResults: Among the acquired signals, the vibration signal obtained from add-on sensor showed better performance (100%, J₃ = 46.6) than the motor current (88.7%, J₃ = 3.89) and angular position (100%, J₃ = 5.88) acquired from the built-in sensor. In the features of the vibration signal, those in the frequency band with strong energy concentration showed outstanding separability.BRConclusion: The proposed method successfully classifies the fault from the normal in the planetary gearbox in the basis of the frequency-domain features of the vibration signal.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
