DEVELOPMENT OF AN ACOUSTIC FAULT DIAGNOSIS SYSTEM FOR UAV PROPELLER BLADES

Kurzfassung

With the rapid growth in demand for drones or unmanned aerial vehicles (UAVs), novel maintenance technologies are essential for ensuring automatic, safe, and reliable operations. This study proposes a fault detection system that utilizes the acoustic signature of UAV propeller blades for classifying their health state. By employing an acoustic camera with 112 microphones for spatial resolution of sound sources, datasets of acoustic images are generated in three differently reverberating environments for the third octave frequency bands of 6300 Hz, 8000 Hz, 10000 Hz and 12500 Hz. A convolutional neural network (CNN) is trained and evaluated with maximum F1-scores of 0.9962 and 0.9745 for two and three propeller health classes, respectively. Furthermore, we propose a second approach based on a linear classification (LC), which utilizes a rotating beamformer for comparison. This approach uses only two sound sources that are identified after the beamforming of a two-bladed propeller. In comparison, this algorithm detects propeller tip damages without applying a machine learning algorithm and reaches an F1-score of 0.9441.