Machine learning methods for multi-rotor UAV structural damage detection based on MEMS sensor

Abstract

Multi-rotor Unmanned Aerial Vehicles (UAVs) have become increasingly important in industries and early detection of structural damage is crucial to prevent unexpected breakdowns, ensure production efficiency, and maintain operational safety. This paper proposes machine learning techniques for detecting damage caused by loosened screws which is not easy founded based on vibration signals. An independent data acquisition device with a Micro Electro Mechanical Systems (MEMS) sensor is designed and fixed onto the multi-rotor UAVs to acquire the vibration data. Four machine learning algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, and Random Forest, are employed for damage detection. The results demonstrate successful utilization of the vibration data from the MEMS sensor for damage detection, with the random forest model outperforming other models with an accuracy of 90.07.