Enhancing UAV Safety through Battery Health Monitoring and Flight Log-Based Crash Diagnostics

Unmanned Aerial Vehicles (UAVs) are increasingly deployed across critical applications, including disaster response, surveillance, and logistics. However, drone crashes caused by battery failures and undetected pre-failure anomalies remain a major concern, particularly in endurance-demanding missions. This paper presents a diagnostic approach for identifying battery-related anomalies by analyzing sensor and telemetry data from hexacopter flight logs. A detailed mathematical model of the UAV's motion dynamics is developed to understand the system's behavior under stable and stressful conditions. The study highlights the role of key onboard sensors, such as current sensors, thermistors, barometric altimeters, and inertial measurement units (IMUs) in power monitoring and pre-crash diagnostics. Three flight scenarios are analyzed: normal operation, operation under wind disturbance, and battery-induced crashes. Results demonstrate that subtle irregularities in current draw, voltage fluctuations, and sensor anomalies can serve as early indicators of system instability. By benchmarking nominal behavior and correlating it with real-time flight data, the proposed method enables proactive detection of failure signatures, improving UAV reliability and safety. This work contributes to the development of sensor-integrated health monitoring systems for autonomous drones.