The continuous evolution of modern technology has led to the creation of increasingly complex and advanced systems. This has been also reflected in the technology of Unmanned Aerial Vehicles (UAVs), where the growing demand for more reliable performance necessitates the development of sophisticated techniques that provide fault diagnosis and fault tolerance in a timely and accurate manner. Typically, a UAV consists of three types of subsystems: actuators, main structure and sensors. Therefore, a fault-monitoring system must be specifically designed to supervise and debug each of these subsystems, so that any faults can be addressed before they lead to disastrous consequences. In this survey article, we provide a detailed overview of recent advances and studies regarding fault diagnosis, Fault-Tolerant Control (FTC) and anomaly detection for UAVs. Concerning fault diagnosis, our interest is mainly focused on sensors and actuators, as these subsystems are mostly prone to faults, while their healthy operation usually ensures the smooth and reliable performance of the aerial vehicle.

The continuous evolution of modern technology has led to the creation of increasingly complex and advanced systems. The growing demands for more reliable performance on such systems as the Unmanned Aerial Vehicles (UAVs), have made it imperative to develop sophisticated techniques that provide fault diagnosis in a timely and accurate manner. It is emphasized that the issues of safe operation of such systems are of utmost importance and their continuous monitoring is required in order to manage emergencies as quickly as possible when faults have occurred. In fault diagnosis, an automated system can be consisted of three main types of subsystems: actuators, main structure and sensors. Typically, a fault monitoring system is specifically designed to supervise and debug each of these subsystems. Successful execution of UAVs missions, is associated with the timely detection and isolation of serious faults occurring to their sensors and actuators. Therefore, it is crucial for the UAV to track its reliable operation so that any faults can be addressed before they lead to disastrous consequences. In this survey article we provide a detailed overview of recent advances and studies regarding fault diagnosis methodologies for UAVs, focusing primarily on sensor and actuator faults.

This paper presents a model based actuator fault diagnosis for a four wheel skid steering mobile robot (SSMR). In this approach we use structural analysis based technique in order to generate residuals. The goal is to detect actuators faults as early as possible and provide a timely warning. For this purpose we develop the kinematic model of the mobile robot that serves to the creation of the structural model of the system. This technique provides the parity equations which can be used as residual generators. The main advantage of the proposed method is that can offers feasible solution to residual generation for nonlinear systems. As a robotic platform, was used the mobile robot Pioneer 3-AT.

This research investigates a sensors fault diagnosis in autonomous mobile robot. Through this study we use the observer - Kalman filter identification technique. According to this observer - based method, the experimental input-output data are being exploited for system identification. The motive is to design observers exclusively for each sensor of the system that allows generating residuals. The research goal is to provide early sensors fault diagnosis. In order to prove the efficacy of the proposed method, we investigated different type of faults and an experimental procedure was carried out using a Pioneer 3AT mobile robot.