Motor Anomaly Detection for Aerial Unmanned Vehicles Using Temperature Sensor

Abstract

Aerial unmanned vehicle is widely used in many fields, such as weather observation, framing, inspection of infrastructure, monitoring of disaster areas. However, the current aerial unmanned vehicle is difficult to avoid falling in the case of failure. The purpose of this article is to develop an anomaly detection system, which prevents the motor from being used under abnormal temperature conditions, so as to prevent safety flight of the aerial unmanned vehicle. In the anomaly detection system, temperature information of the motor is obtained by DS18B20 sensors. Then, the reinforcement learning, a type of machine learning, is used to determine the temperature is abnormal or not by Raspberrypi processing unit. We also build an user interface to open the screen of Raspberrypi on laptop for observation. In the experiments, the effectiveness of the proposed system to stop the operation state of drone when abnormality exceeds the automatically learned motor temperature. The experimental results demonstrate that the proposed system is possibility for unmanned flight safely by controlling drone from information obtained by attaching temperature sensors.