A Novel Fault Detection Approach in UAV With Adaptation of Fuzzy Logic and Sensor Fusion

Unmanned aerial vehicles (UAVs) or UAVs are increasingly applied by military and civilians in a wide range of applications, such as environmental monitoring, search and rescue, target detection, combat, precision agriculture, and three-dimensional (3-D) mapping. The failure of UAVs can cause human casualties and property damage. Hence, it is crucial to consistently monitor the UAV in order to detect any potential malfunction in vital components like motors and electronic speed controllers (ESCs). This study proposed a new UAV condition-monitoring approach that combines sensor fusion and fuzzy-based decision-making algorithms. The vibration, the motor's rotational speed, and current parameters are utilized to determine the UAV's condition, specifically the UAV's motors or ESCs. The UAV's condition is categorized into safe, partial safe, and danger. Experimental results show a clear distinction between a healthy and faulty UAV's component in terms of vibration, with a discrepancy of more than 100% in the majority of the cases. The proposed framework can successfully alert the user if there is a malfunction in the UAV's motor or ESC before or when the UAV is flying.