The next revolution in aviation is upon us with advances in novel configurations of electric vertical take-off and landing (eVTOL) aircraft. Advanced air mobility (AAM) will offer on-demand services for human and cargo transport and package delivery in the major cities of the world. Its other anticipated uses include surveillance for public safety, humanitarian aid, infrastructure supervision, remote sensing, etc. However, the operational success of mass transportation services by aerial vehicles will require absolute safety and reliability making efficient health and usage monitoring (HUMS) of these systems vital. According to a technical report by Uber Elevate, the safety level in air-taxi aviation needs to improve from 1.2 to 0.3 fatalities per 100 million passenger miles through full autonomy and innovation, with large amounts of data from real-world operations after the first generation VTOL aircraft are in production. Therefore, research and development are imperative to realize real-time system-level awareness and decision-making, in future intelligent and autonomous VTOL aircraft. This line of work focuses on fault detection and identification (FDI) of system faults in potential AAM vehicles utilizing in-flight data streams. Knowledge of system faults in real-time is critical for control reallocation or vehicle reconfiguration to complete the flight safely. Moreover, the incorporation of condition monitoring from the early phases of eVTOL operation will boost aircraft readiness, enhance flight safety, and lower maintenance, and operating costs. These will ensure the commercial success of the large fleet of frequently flying aerial vehicles. There has been extensive research going on to implement fault-tolerant control on multirotor aircraft, most of which relies on information about system faults in real-time to switch onto more power-efficient optimum control schemes or plan alternate trajectories with limited control authority awareness, depending upon the type and extent of faults. However, the analytical FDI approaches are mostly limited by the requirement of in-depth physical knowledge of the aircraft and lack of efficient handling of noise, and uncertainty, while the data-driven approaches suffer from a lack of explainability due to focusing on fitting the data and concentrate mostly on structural faults in blades and powertrain components of single rotor platforms. Therefore, the research gap related to probabilistic actuator FDI has been explored in this thesis.
In this study, the following challenges pertaining to the development of a probabilistic multicopter FDI technique have been addressed. First, it should be robust under operational variability, environmental disturbances, and uncertainty. Second, online fault monitoring should be made possible by improving the run-time of the decision-making scheme through low-dimensional representations of the dynamic information contained in the multi-modal sensory data. Third, these low-dimensional representations must be physically explainable, based on stochastic representations of the multicopter dynamics contained in data streams (aircraft states and controls time-series data).
Therefore, in the context of probabilistic FDI, a stochastic framework for FDI in multicopters is proposed, which attains the goal of being accurate, robust, and data-driven with improved physical interpretability. Its cornerstone lies in ``global'' stochastic time-series models which can appropriately represent the dynamics of the aircraft flight signals under multiple flight states, different fault types and magnitude, changing environmental disturbances, and uncertainty via functional pooling of data. At first, residual-based statistical time-series methods have been investigated with a novel application to multicopter rotor FDI. Some of these methods exhibit excellent accuracy but suffer from certain limitations that have been addressed through an innovative approach that integrates statistical time-series modeling and a machine learning algorithm. This method, titled the time-series assisted neural network has the following advantages over the former: (i) it requires only the healthy stochastic model to derive fault-sensitive (type and magnitude), and disturbance-rejecting features, (ii) it makes probabilistic decisions regarding the rotor faults in a single step using a simple neural network, (iii) it is applicable throughout the entire flight regime and has better accuracy with shorter signals enabling faster FDI.
In the second part of this thesis, flexible booms have been incorporated into the multicopter to generate simulated data. This opened new avenues for signal selection from remote and local sensors to achieve better uncertainty quantification in rotor fault magnitude estimation. It was achieved via inverse optimization techniques with the aforementioned ``global" stochastic models representing the functional dependence of signal dynamics with varying fault magnitude. Exploring local sensors mounted on the booms also led to the development of a probabilistic rotor fault diagnosis framework based on simple machine learning algorithms. It was developed using out-of-plane strain signals at individual boom roots and exhibited over 99\% rotor FDI accuracy under any admissible operating conditions and external disturbances without the need for dynamic representations or knowledge of the operating states. In the final task, the time-series assisted neural network performance was validated with experimental data from flight tests of a quadcopter and a hexacopter.;
August 2022; School of Engineering
Dept. of Mechanical, Aerospace, and Nuclear Engineering;
Rensselaer Polytechnic Institute, Troy, NY
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