Data-Driven Fault Detection of Un-Manned Aerial Vehicles Using Supervised Learning Over Cloud Networks

Modern applications of Unmanned Aerial Vehicles are increasingly attracting the attention of traditional safety and reliability fields. There exist many standard approaches for determining UAV fault detection. However, there doesn't exist a method that is not only model independent but also has the ability to detect faults which have not been predefined for the UAV system. In this research we present two supervised machine learning algorithms implementing Logistic Regression and Linear Discriminant Analysis of Algorithms, respectively, to predict UAV faults. The data which has been used for these approaches comes from discrete-sampled, de-noised analog signals based on the voltage and current inputs belonging to four actuators of the UAV drones. In addition, we demonstrate that by using a five-fold cross validation process to generate different types of training and test datasets, the optimized model can be selected. We verify our results through an analysis describing the accuracies of our proposed model.