A conceptual design of a propulsion system for an autonomous underwater vehicle

The need for developing propulsion systems to support missions of increased endurance for autonomous underwater vehicles is investigated and a conceptual system is proposed, based on currently available technology and desired system characteristics. The investigation evaluates and ranks alternative energy sources and proposes the use of a closed Brayton cycle gas turbine power plant using a chemical energy heat source with a metallic fuel. A thruster system using electric propulsion motors and screw propellers is selected. Evaluation factors include reliability, depth independent operation, weight, endurance, quietness and efficiency. Reliability of the proposed system is analyzed and the design modified to meet proposed reliability requirements. A knowledge-based system is developed to manage the operation of the propulsion plant in an autonomous manner. A simulation system is developed using Common Lisp and the operation of the propulsion plant and its knowledge-based management system are evaluated using the simulator

Increasing the robustness of autonomous systems to hardware degradation using machine learning

Autonomous systems perform predetermined tasks (missions) with minimum supervision. In most applications, the state of the world changes with time. Sensors are employed to measure part or whole of the world’s state. However, sensors often fail amidst operation; feeding as such decision-making with wrong information about the world. Moreover, hardware degradation may alter dynamic behaviour, and subsequently the capabilities, of an autonomous system; rendering the original mission infeasible. This thesis applies machine learning to yield powerful and robust tools that can facilitate autonomy in modern systems. Incremental kernel regression is used for dynamic modelling. Algorithms of this sort are easy to train and are highly adaptive. Adaptivity allows for model adjustments, whenever the environment of operation changes. Bayesian reasoning provides a rigorous framework for addressing uncertainty. Moreover, using Bayesian Networks, complex inference regarding hardware degradation can be answered. Specifically, adaptive modelling is combined with Bayesian reasoning to yield recursive estimation algorithms that are robust to sensor failures. Two solutions are presented by extending existing recursive estimation algorithms from the robotics literature. The algorithms are deployed on an underwater vehicle and the performance is assessed in real-world experiments. A comparison against standard filters is also provided. Next, the previous algorithms are extended to consider sensor and actuator failures jointly. An algorithm that can detect thruster failures in an Autonomous Underwater Vehicle has been developed. Moreover, the algorithm adapts the dynamic model online to compensate for the detected fault. The performance of this algorithm was also tested in a real-world application. One step further than hardware fault detection, prognostics predict how much longer can a particular hardware component operate normally. Ubiquitous sensors in modern systems render data-driven prognostics a viable solution. However, training is based on skewed datasets; datasets where the samples from the faulty region of operation are much fewer than the ones from the healthy region of operation. This thesis presents a prognostic algorithm that tackles the problem of imbalanced (skewed) datasets

Predicción de la energía renovable proveniente del oleaje en las islas de Fuerteventura y Lanzarote

Ministerio de Economía y Competitividad; DPI2015-69325- C2-2-R[Resumen] En el siguiente trabajo de investigación se realiza una caracterización de los potenciales del oleaje en las islas de Fuerteventura y Lanzarote, llevando a cabo una modelización de los mismos. Para alcanzar dicho objetivo se utilizan las series temporales del oleaje para aguas profundas facilitadas por “Puertos del Estado” (1996- 2016). El estudio establece un mapa de recursos undimotrices a partir de la información disponible, determinando zonas de interés que posteriormente podrán ser analizadas con mayor precisión. Se presenta un sistema NeuroFuzzy genético con el objetivo de predecir los valores energéticos undimotrices de ciertos puntos, a partir de los valores conocidos de los puntos WANA cercanos

Robotics handbook. Version 1: For the interested party and professional

This publication covers several categories of information about robotics. The first section provides a brief overview of the field of Robotics. The next section provides a reasonably detailed look at the NASA Robotics program. The third section features a listing of companies and organization engaging in robotics or robotic-related activities; followed by a listing of associations involved in the field; followed by a listing of publications and periodicals which cover elements of robotics or related fields. The final section is an abbreviated abstract of referred journal material and other reference material relevant to the technology and science of robotics, including such allied fields as vision perception; three-space axis orientation and measurement systems and associated inertial reference technology and algorithms; and physical and mechanical science and technology related to robotics

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Towards Fault Diagnosis in Reconfigurable Robot

東京電機大学201

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations