Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item APPLICATION OF A BAYESIAN NETWORK BASED FAILURE DETECTION AND DIAGNOSIS FRAMEWORK ON MARITIME DIESEL ENGINES(2022) Reynolds, Steven; Groth, Katrina; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Diesel engine propulsion has been the largest driver of maritime trade and transportation since its development in the early 20th century and the technology surrounding the operation and maintenance of these systems has grown in complexity leading to rapid advancement in amount and variety of data being collected. This increase in reliability data provides a fantastic opportunity to improve upon the existing tools troubleshooting and decision support tool used within the maritime engine community to enable a more robust understanding of engine reliability. This work leverages this opportunity and applies it to the Coast Guard and its acquisition of the Fast Response Cutter (FRC) fleet powered by two MTU20V4000M93 engines integrated with top of line monitoring and control equipment.The purpose of this research is to create procedures for creating a Failure Detection and Diagnosis (FDD) model of a maritime diesel engine that updates existing Probabilistic Risk Analysis (PRA) data with input from the engine monitoring and control system using Bayesian inference. A literature review of existing work within the PRA and Prognostics and Health Management (PHM) fields was conducted with specific focus on the advancement and gaps in the field specific to their use in maritime engine applications. Following this, a hierarchal ruleset was created that outlines procedures for integrating existing PRA data and PHM metrics into a Bayesian Network structure. This methodology was then used to build a Bayesian Network based FDD model of the FRC engine. This model was then validated by Coast Guard Engineers and run through a diagnostic use case scenario demonstrating the model’s suitability in the diagnostic space.Item MECHANISM DESIGN WITH GENERAL UTILITIES(2012) Alaei, Saeed; Khuller, Samir; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis studies mechanism design from an optimization perspective. Our main contribution is to characterize fundamental structural properties of optimization problems arising in mechanism design and to exploit them to design general frameworks and techniques for efficiently solving the underlying problems. Not only do our characterizations allow for efficient computation, they also reveal qualitative characteristics of optimal mechanisms which are important even from a non-computational standpoint. Furthermore, most of our techniques are widely applicable to optimization problems outside of mechanism design such as online algorithms or stochastic optimization. Our frameworks can be summarized as follows. When the input to an optimization problem (e.g., a mechanism design problem) comes from independent sources (e.g., independent agents), the complexity of the problem can be exponentially reduced by (i) decomposing the problem into smaller subproblems, each one involving one input source, (ii) simultaneously optimizing the subproblems subject to certain relaxation of coupling constraints, and (iii) combining the solutions of the subproblems in a certain way to obtain an (approximately) optimal solution for the original problem. We use our proposed framework to construct optimal or approximately optimal mechanisms for several settings previously considered in the literature and to improve upon the best previously known results. We also present applications of our techniques to non-mechanism design problems such as online stochastic generalized assignment problem which itself captures online and stochastic versions of various other problems such as resource allocation and job scheduling.