With the rapid growth in demand for unmanned aerial vehicles (UAVs), novel maintenance technologies are essential for ensuring automatic, safe, and reliable operations. This study compares two fault detection systems that utilize 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, 10,000 Hz and 12,500 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 acoustic beamforming of a two-bladed propeller. In comparison, this algorithm detects propeller tip damages without applying a machine learning algorithm and reaches a slightly lower F1-score of 0.9441.