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No description available.

No description available.

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The area of autonomous underwater vehicles (AUVs) is an increasingly important area of research, with AUVs being capable of handling a far wider range of missions than either an inhabited underwater vehicle or a remotely operated vehicle (ROV). One of the major drawbacks of such vehicles is the inability of their control systems to handle faults occurring within the vehicle during a mission. This study aims to develop enhancements to an existing control system in order to increase its fault tolerance to both sensor and actuator faults. Faults occurring within the sensors for both the yaw and roll channels of the AUV are considered. Novel fuzzy inference systems (FISs) are developed and tuned using both the adaptive neuro-fuzzy inference system (ANFIS) and simulated annealing tuning methods. These FISs allow the AUV to continue operating after a fault has occurred within the sensors. Faults occurring within the actuators which control the canards of the AUV and hence the yaw channel are also examined. Actuator recovery FISs capable of handling faults occurring within the actuators are developed using both the simulated annealing and tabu search methods of tuning FISs. The fault tolerance of the AUV is then further enhanced by the development of an error estimation FIS that is used to replace an error sensor. It concludes that the novel FISs designed and developed within the thesis provide an improved performance to both sensor and actuator faults in comparison to benchmark control systems. Therefore having these FISs embedded within the overall control scheme ensure the AUV is fault tolerant to a range of selected failures.

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No description available.

For ship systems to remain reliable and safe they must be effectively maintained through a sound maintenance management system. The three major elements of maintenance management systems are; risk assessment, maintenance strategy selection and maintenance task interval determination. The implementation of these elements will generally determine the level of ship system safety and reliability. Reliability Centred Maintenance (RCM) is one method that can be used to optimise maintenance management systems. However the tools used within the framework of the RCM methodology have limitations which may compromise the efficiency of RCM in achieving the desired results. This research presents the development of tools to support the RCM methodology and improve its effectiveness in marine maintenance system applications. Each of the three elements of the maintenance management system has been considered in turn. With regard to risk assessment, two Multi-Criteria Decision Making techniques (MCDM); Vlsekriterijumska Optimizacija Ikompromisno Resenje, meaning: Multi-criteria Optimization and Compromise Solution (VIKOR) and Compromise Programming (CP) have been integrated into Failure Mode and Effects Analysis (FMEA) along with a novel averaging technique which allows the use of incomplete or imprecise failure data. Three hybrid MCDM techniques have then been compared for maintenance strategy selection; an integrated Delphi-Analytical Hierarchy Process (AHP) methodology, an integrated Delphi-AHP-PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluation) methodology and an integrated Delphi-AHP-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methodology. Maintenance task interval determination has been implemented using a MCDM framework integrating a delay time model to determine the optimum inspection interval and using the age replacement model for the scheduled replacement tasks. A case study based on a marine Diesel engine has been developed with input from experts in the field to demonstrate the effectiveness of the proposed methodologies.