This book provides a solution to the control and motion planning design for an octocopter system. It includes a particular choice of control and motion planning algorithms which is based on the authors' previous research work, so it can be used as a reference design guidance for students, researchers as well as autonomous vehicles hobbyists. The control is constructed based on a fault tolerant approach aiming to increase the chances of the system to detect and isolate a potential failure in order to produce feasible control signals to the remaining active motors. The used motion planning algorithm is risk-aware by means that it takes into account the constraints related to the fault-dependant and mission-related maneuverability analysis of the octocopter system during the planning stage. Such a planner generates only those reference trajectories along which the octocopter system would be safe and capable of good tracking in case of a single motor fault and of majority of double motor fault scenarios. The control and motion planning algorithms presented in the book aim to increase the overall reliability of the system for completing the mission.

The Meantime To Failure (MTTF) is a statistic used for system analysis in several knowledge areas. This value represents the average time to the system enters into one of the possible states of fault, without considering system repairs. Although MTTF be considered to analyze systems with fault states, it also can be used to perform analysis on processes, since it can be used to represent the meantime to one process finishes, given that, processes can be represented by state machine models. This work presents a method to compute MTTF from Continuous Time Markov Chain (CTMC) models. There are no arguments that demonstrate that this method performs better than other methods, but this method has a simpler implementation and is intuitive. This method also allows computing the absorption probabilities and the average holding time of each state without additional steps.

The cost of wind energy can be reduced by using SCADA data to detect faults in wind turbine components. Normal behavior models are one of the main fault detection approaches, but there is a lack of consensus in how different input features affect the results. In this work, a new taxonomy based on the causal relations between the input features and the target is presented. Based on this taxonomy, the impact of different input feature configurations on the modelling and fault detection performance is evaluated. To this end, a framework that formulates the detection of faults as a classification problem is also presented.