Effect of Mixing Time and Frequency-Domain Objectives in Detecting Problematic Vibration on Unmanned Aerial Vehicles via Barnicle Mating Optimization

Problematic vibration detection from Unmanned Aerial Vehicles (UAVs) as a flaw detection and identification (FDI) method has become a viable instrument for evaluating the health and condition of a UAV. The goal of this work is to demonstrate the impact of integrating frequency-domain and time-domain analysis as time-domain and frequency-domain objectives. a suggested fitness function that combines the time and frequency domains with a mixing variable. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and detection time are used to test and assess the fitness function with the Barnicle Mating optimization (BMO) Algorithm optimization technique. To evaluate the efficacy of the suggested fitness function and BMO, 51 sets of data were gathered using software in the loop (SITL) techniques. The test findings demonstrate that the best MAE range outcomes with the least increase in RMSE and detection time were obtained by combining 25% and 75% of the time- and frequency-domain objectives, respectively.