Supervised Learning Classification Applications in Fault Detection and Diagnosis: An Overview of Implementations in Unmanned Aerial Systems 01-16-01-0004

Statistical machine learning classification methods have been widely used in the fault detection analysis in several engineering domains. This motivates us to provide in this article an overview on the application of these methods in the fault diagnosis strategies and also their successful use in unmanned aerial vehicles (UAVs) systems. Different existing aspects including the implementation conditions, offline design, and online computation algorithms as well as computation complexity and detection time are discussed in detail. Evaluation and validation of these aspects have been ensured by a simple demonstration of the basic classification methods and neural network techniques in solving the fault detection and diagnosis problem of the propulsion system failure of a multirotor UAV. A testing platform of an Hexarotor UAV is completely realized. Measurements data issued from the onboard sensors are collected and a classification model to detect damaged propellers and failed motors has been built. To simulate a motor fault condition, its effectiveness is reduced using an RC transmitter. Propeller damages are simulated by clipping the propellers gradually. Experimental results demonstrate that artificial neural networks (ANN) techniques outperform other methods in terms of classification accuracy and are shown to be effective at identifying the different types of damaged propellers or failed motors.