Rotor blades are the primary cause of drone failure. To assess the health status of drone blades, vibration monitoring is required; however, this is challenging due to noisy signals and limited labeled datasets. This study investigates a data loading strategy called selective embedding (SE), which is shown to improve data diagnosis across engineering fields. The hypothesis is that this strategy can improve the classification accuracy of drone blade conditions with multi-axis vibration data. Accelerometer signals are collected under different blade health conditions; the signals are then processed and fed into a deep learning model for multi class condition classification. An ablation study is conducted with different data loading strategies, including traditional single channel, parallel channel, and SE. The results show that SE improves classification accuracy, reduces performance variance, and achieves higher generalization performance across multiple blade fault conditions. These improvements are observed consistently across domain evaluations, where traditional data loading strategies have difficulty generalizing to unseen temporal segments. The findings demonstrate that SE can effectively support vibration fault diagnostics for aerospace applications, offering a reliable way to improve safety in drone monitoring.
Sehri et al. (Fri,) studied this question.