Ensuring the safety and efficiency of unmanned aerial vehicles (UAVs) requires effective fault detection and identification (FDI). Traditional multi-stage FDI methods, particularly those using residual detection layers, increase complexity and computational cost, limiting real-time applications. This study proposes a single-stage anomaly detection framework integrating barnacle mating optimization (BMO) with discrete cosine transform (DCT) for UAV fault detection. While prior research explored model-based and data-driven FDI, bio-inspired optimization techniques remain underexplored in frequency-domain analysis. This study develops a BMO-based fitness function analyzing 3rd, 5th, and 7th harmonic peaks to detect UAV anomalies. Software-in-the-Loop (SITL) simulations validate the method, achieving a 5-second optimal frame size, mean absolute percentage error (MAPE) of 0.05, and root mean square error (RMSE) of 195.52. The findings confirm that a single-stage detection framework via optimization method and frequency domain analysis is possible, making it viable for real-time UAV applications. This study bridges the gap in bio-inspired UAV fault detection, paving the way for safer and more efficient UAV operations.
Zakaria et al. (Fri,) studied this question.