Real-time propeller fault detection for multirotor drones based on vibration data analysis

• Published in: Engineering applications of artificial intelligence Vol. 123; p. 106343
• Main Authors: Baldini, Alessandro, Felicetti, Riccardo, Ferracuti, Francesco, Freddi, Alessandro, Iarlori, Sabrina, Monteriù, Andrea
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Fault detection; Signal processing; Unmanned aerial vehicles; Fault detection; Signal processing; Unmanned aerial vehicles
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2023.106343

This article presents a Fault Detection (FD) method to deal with propeller faults on multirotor drones in real-time. Several solutions have been proposed in the literature, however, they depend on additional sensors and/or dedicated hardware to deal with heavy computational complexity. So, they cannot be implemented in off-the-shelf commercial devices, i.e., without the aid of additional on-board sensors and/or extra computational power. The proposed method, instead, requires the on-board Inertial Measurement Unit (IMU) data only: by combining Finite Impulse Response (FIR), together with sparse classifiers, only a subset of the features is actually needed online and the FD is thus feasible in real-time. Design and tests are based on real flight data from a hexarotor, equipped with a conventional ArduPilot-based controller. The classification accuracy in testing is up to 93.37% (98.21%) with a binary tree (Linear Support Vector Machine (LSVM)). Moreover, the space and time complexity of the proposed method is low: on a PixHawk Cube flight controller, it requires less than 2% of the cycle time, and can then run in real-time. Finally, the proposed fault detection solution is model-free and it can be easily generalized to other multirotor vehicles.