UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item A Framework for Remaining Useful Life Prediction and Optimization for Complex Engineering Systems(2024) Weiner, Matthew Joesph; Azarm, Shapour; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Remaining useful life (RUL) prediction plays a crucial role in maintaining the operational efficiency, reliability, and performance of complex engineering systems. Recent efforts have primarily focused on individual components or subsystems, neglecting the intricate relationships between components and their impact on system-level RUL (SRUL). The existing gap in predictive methodologies has prompted the need for an integrated approach to address the complex nature of these systems, while optimizing the performance with respect to these predictive indicators. This thesis introduces a novel methodology for predicting and optimizing SRUL, and demonstrates how the predicted SRUL can be used to optimize system operation. The approach incorporates various types of data, including condition monitoring sensor data and component reliability data. The methodology leverages probabilistic deep learning (PDL) techniques to predict component RUL distributions based on sensor data and component reliability data when sensor data is not available. Furthermore, an equation node-based Bayesian network (BN) is employed to capture the complex causal relationships between components and predict the SRUL. Finally, the system operation is optimized using a multi-objective genetic algorithm (MOGA), where SRUL is treated as a constraint and also as an objective function, and the other objective relates to mission completion time. The validation process includes a thorough examination of the component-level methodology using the C-MAPSS data set. The practical application of the proposed methodology in this thesis is through a case study involving an unmanned surface vessel (USV), which incorporates all aspects of the methodology, including system-level validation through qualitative metrics. Evaluation metrics are employed to quantify and qualify both component and system-level results, as well as the results from the optimizer, providing a comprehensive understanding of the proposed approach’s performance. There are several main contributions of this thesis. These include a new deep learning structure for component-level PHM, one that utilizes a hybrid-loss function for a multi-layer long short-term memory (LSTM) regression model to predict RUL with a given confidence interval while also considering the complex interactions among components. Another contribution is the development of a new framework for computing SRUL from these predicted component RULs, in which a Bayesian network is used to perform logic operations and determine the SRUL. These contributions advance the field of PHM, but also provide a practical application in engineering. The ability to accurately predict and manage the RUL of components within a system has profound implications for maintenance scheduling, cost reduction, and overall system reliability. The integration of the proposed method with an optimization algorithm closes the loop, offering a comprehensive solution for offline planning and SRUL prediction and optimization. The results of this research can be used to enhance the efficiency and reliability of engineering systems, leading to more informed decision-making.Item An Approximation Framework for Large-Scale Spatial Games(2023) Hsiao, Vincent; Nau, Dana; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Game theoretic modeling paradigms such as Evolutionary Games and Mean Field Games (MFG) are used to model a variety of multi-agent systems in which the agents interact in a game theoretic fashion. These models seek to answer two questions: how to predict the forward dynamics of a population and how to control them. However, both modeling paradigms have unique issues that can make them difficult to analyze in closed form when applied to spatial domains. On one hand, spatial EGT models are difficult to evaluate mathematically and both simulations and approximations run into accuracy and tractability issues. On the other hand, MFG models are not typically formulated to handle domains where agents have strategies and physical locations. Furthermore, any MFG approach for controlling strategy evolution on spatial domains need also address the same accuracy and efficiency challenges in the evaluation of its forward dynamics as those faced by evolutionary game approaches. This dissertation presents a new modeling paradigm and approximation technique termed Bayesian-MFG for large-scale multi-agent games on spatial domains. The new framework lies at an intersection of techniques drawn from spatial evolutionary games, mean field games, and probabilistic reasoning. First, we describe our Bayesian network approximation technique for spatial evolutionary games to address the accuracy issues faced by lower order approximation methods. We introduce additional algorithms used to improve the computational efficiency of Bayesian network approximations. Alongside this, we describe our Pair-MFG model, a method for defining pair level approximate MFG for problems with distinct strategy and spatial components. We combine the pair-MFG model and Bayesian network approximations into a unified Bayesian-MFG framework. Using this framework, we present a method for incorporating Bayesian network approximations into a control problem framework allowing for the derivation of more accurate control policies when compared to existing MFG approaches. We demonstrate the effectiveness of our framework through its application to a variety of domains such as evolutionary game theory, reaction-diffusion equations, and network security.Item A Methodology for Project Risk Analysis using Bayesian Belief Networks within a Monte Carlo Simulation Environment(2007-04-26) Ordonez Arizaga, Javier F.; Baecher, Gregory B; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Projects are commonly over budget and behind schedule, to some extent because uncertainties are not accounted for in cost and schedule estimates. Research and practice is now addressing this problem, often by using Monte Carlo methods to simulate the effect of variances in work package costs and durations on total cost and date of completion. However, many such project risk approaches ignore the large impact of probabilistic correlation on work package cost and duration predictions. This dissertation presents a risk analysis methodology that integrates schedule and cost uncertainties considering the effect of correlations. Current approaches deal with correlation typically by using a correlation matrix in input parameters. This is conceptually correct, but the number of correlation coefficients to be estimated grows combinatorially with the number of variables. Moreover, if historical data are unavailable, the analyst is forced to elicit values for both the variances and the correlations from expert opinion. Most experts are not trained in probability and have difficulty quantifying correlations. An alternative is the integration of Bayesian belief networks (BBN's) within an integrated cost-schedule Monte Carlo simulation (MCS) model. BBN's can be used to implicitly generate dependency among risk factors and to examine non-additive impacts. The MCS is used to model independent events, which are propagated through BBN's to assess dependent posterior probabilities of cost and time to completion. BBN's can also include qualitative considerations and project characteristics when soft evidence is acquired. The approach builds on emerging methods of systems reliability.