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.
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Item A CAUSAL INFORMATION FUSION MODEL FOR ASSESSING PIPELINE INTEGRITY IN THE PRESENCE OF GROUND MOVEMENT(2024) Schell, Colin Andrew; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Pipelines are the primary transportation method for natural gas and oil in the United States making them critical infrastructure to maintain. However, ground movement hazards, such as landslides and ground subsidence, can deform pipelines and potentially lead to the release of hazardous materials. According to the Pipeline and Hazardous Materials Safety Administration (PHMSA), from 2004 to 2023, ground movement related pipeline failures resulted in $413M USD in damages. The dynamic nature of ground movement makes it necessary to collect pipeline and ground monitoring data and to actively model and predict pipeline integrity. Conventional stress-based methods struggle to predict pipeline failure in the presence of large longitudinal strains that result from ground movement. This has prompted many industry analysts to use strain-based design and assessment (SBDA) methods to manage pipeline integrity in the presence of ground movement. However, due to the complexity of ground movement hazards and their variable effects on pipeline deformation, current strain-based pipeline integrity models are only applicable in specific ground movement scenarios and cannot synthesize complementary data sources. This makes it costly and time-consuming for pipeline companies to protect their pipeline network from ground movement hazards. To close these gaps, this research made significant steps towards the development of a causal information fusion model for assessing pipeline integrity in a variety of ground movement scenarios that result in permanent ground deformation. We developed a causal framework that categorizes and describes how different risk-influencing factors (RIFs) affect pipeline reliability using academic literature, joint industry projects, PHMSA projects, pipeline data, and input from engineering experts. This framework was the foundation of the information fusion model which leverages SBDA methods, Bayesian network (BN) models, pipeline monitoring data, and ground monitoring data to calculate the probability of failure and the additional longitudinal strain needed to fail the pipeline. The information fusion model was then applied to several case studies with different contexts and data to compare model-based recommendations to the actions taken by decision makers. In these case studies, the proposed model leveraged the full extent of data available at each site and produced similar conclusions to those made by decision makers. These results demonstrate that the model could be used in a variety of ground movement scenarios that result in permanent ground deformation and exemplified the comprehensive insights that come from using an information fusion approach for assessing pipeline integrity. The proposed model lays the foundation for the development of advanced decision making tools that can enable operators to identify at-risk pipeline segments that require site specific integrity assessments and efficiently manage the reliability of their pipelines in the presence of ground movement.Item MANUFACTURABILITY AND RELIABILITY OF ADDITIVELY MANUFACTURED PLANAR TRANSFORMER WINDINGS USING SILVER-BASED PASTE(2023) Yun, He; McCluskey, F. Patrick; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation is primarily concerned with the integration of additive manufacturing (AM) techniques into planar magnetics to achieve more efficient designs for power modules, which are in high demand. The two main focuses of this dissertation are: (1) the use of a paste-based AM technique called syringe-printing to create planar transformer windings without the need for pressure, using silver-based paste. The dissertation will address manufacturing considerations such as trace width, gaps, and heights that are printable, as well as the impact of electrical resistivity on the sintering process for the syringe-printed silver-based windings; and (2) the evaluation of the reliability of the syringe-printed silver-based windings, which will involve assessing adhesion performance between the metal/ceramic interface, conducting accelerated life tests (including thermal aging and thermal cycling tests), and identifying failure modes, failure sites, failure mechanisms, and developing degradation/failure models.In order to achieve the desired printing geometry in terms of width and gaps between segments, printing settings were studied parametrically by fitting targeted values with actual values. A low-temperature sintering profile was optimized, with a dwell time of 8 hours at 350°C resulting in a resistivity as low as 4.39E-8 Ω∙m, which was approximately 2.5 times higher than bulk silver. To improve bonding prior to syringe-printing the silver-based windings, it was suggested that an adhesive layer consisting of titanium (Ti) and silver (Ag) be deposited onto the alumina substrate. A degradation model was developed for thermal aging tests. Two batches of single-layer 7-turn syringe-printed windings were subjected to thermal cycling tests, and the corresponding failure modes and mechanisms were investigated. The failure data was used to combine with the strain-energy density extracted from the finite element simulation to develop the fatigue model, with the Coffin-Mason model being fitted for future comparison. A more conservative model could be recommended for real-world applications. Finally, the silver-based paste was syringe-printed onto a cooler with a limited footprint area, which served as the primary and secondary planar transformer board and was used in a 10 kW DC-DC full-bridge power converter with 97% efficiency. Corresponding thermal and electrical performance were discussed.Item Application of Diagnostics and Prognostics Techniques to Qualification Against Wear-Out Failure(2022) Ram, Abhishek; Das, Diganta; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)QUALIFICATION IS A PROCESS THAT DEMONSTRATES WHETHER A PRODUCT MEETS OR EXCEEDS SPECIFIED REQUIREMENTS. TESTING AND DATA ANALYSIS PERFORMED WITHIN A QUALIFICATION PROCEDURE SHOULD VERIFY IF PRODUCTS SATISFY THOSE REQUIREMENTS, INCLUDING RELIABILITY REQUIREMENTS. MOST OF THE ELECTRONICS INDUSTRY QUALIFIES PRODUCTS USING PROCEDURES DICTATED WITHIN QUALIFICATION STANDARDS. A REVIEW OF COMMON QUALIFICATION STANDARDS REVEALS THAT THOSE STANDARDS DO NOT CONSIDER CUSTOMER REQUIREMENTS OR THE PRODUCT PHYSICS-OF-FAILURE IN THAT INTENDED APPLICATION. AS A RESULT, QUALIFICATION, AS REPRESENTED IN THE REVIEWED QUALIFICATION STANDARDS, WOULD NOT MEET OUR DEFINITION OF QUALIFICATION FOR RELIABILITY ASSESSMENT. THIS THESIS PROVIDES AN APPLICATION-SPECIFIC APPROACH FOR DEVELOPING A QUALIFICATION PROCEDURE THAT ACCOUNTS FOR CUSTOMER REQUIREMENTS, PRODUCT PHYSICS-OF-FAILURE, AND KNOWLEDGE OF PRODUCT BEHAVIOR UNDER LOADING. THIS THESIS PROVIDES A REVAMPED APPROACH FOR DEVELOPING A LIFE CYCLE PROFILE THAT ACCOUNTS FOR LOADING THROUGHOUT MANUFACTURING/ASSEMBLY, STORAGE AND TRANSPORTATION, AND OPERATION. THE THESIS ALSO DISCUSSES IDENTIFYING VARIATIONS IN THE LIFE CYCLE PROFILE THAT MAY ARISE THROUGHOUT THE PRODUCT LIFETIME AND METHODS FOR ESTIMATING LOADS. THIS UPDATED APPROACH FOR DEVELOPING A LIFE CYCLE PROFILE SUPPORTS BETTER FAILURE PRIORITIZATION, TEST SELECTION, AND TEST CONDITION AND DURATION REQUIREMENT ESTIMATION. ADDITIONALLY, THIS THESIS INTRODUCES THE APPLICATION OF DIAGNOSTICS AND PROGNOSTICS TECHNIQUES TO ANALYZE REAL-TIME DATA TRENDS WHILE CONDUCTING QUALIFICATION TESTS. DIAGNOSTICS TECHNIQUES IDENTIFY ANOMALOUS BEHAVIOR EXHIBITED BY THE PRODUCT, AND PROGNOSTICS TECHNIQUES FORECAST HOW THE PRODUCT WILL BEHAVE DURING THE REMAINDER OF THE QUALIFICATION TEST AND HOW THE PRODUCT WOULD HAVE BEHAVED IF THE TEST CONTINUED. AS A RESULT, COMBINING DIAGNOSTICS AND PROGNOSTICS TECHNIQUES CAN ENABLE THE PREDICTION OF THE REMAINING TIME-TO-FAILURE FOR THE PRODUCT UNDERGOING QUALIFICATION. SEVERAL ANCILLARY BENEFITS RELATED TO AN IMPROVED TESTING STRATEGY, PARTS SELECTION AND MANAGEMENT, AND SUPPORT OF A PROGNOSTICS AND HEALTH MANAGEMENT SYSTEM IN OPERATION ALSO ARISE FROM APPLYING PROGNOSTICS AND DIAGNOSTICS TECHNIQUES TO QUALIFICATION.Item ESTIMATING THE RELIABILITY OF A NEW CONSUMER PRODUCT USING USER SURVEY DATA AND RELIABILITY TEST DATA(2022) Shafiei, Neda; Modarres, Mohammad; Herrmann, Jeffrey W.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Because new products enter the market rapidly, estimating their reliability is challenging due to insufficient historical data. User survey data about similar devices (e.g., older versions of the new device) can be used as the prior information in a Bayesian analysis integrated with evidence in the form of product returns, reliability tests, and other reliability data sources to improve reliability estimation and test specification of the new product. User surveys are usually designed for purposes other than reliability estimation. Therefore, extracting reliability information from these surveys may be tricky or impossible. Even when possible, the extracted reliability information contains significant uncertainties. This dissertation introduces the critical elements of a reliability-informed user survey and offers methods for collecting them. A generic and flexible mathematical approach is then proposed. This approach uses the survey and reliability test data of similar products, for example, an older generation of the same product as prior knowledge. Then it combines them through a formal Bayesian analysis with the reliability test data to estimate the life distribution of the new product. The approach models continuous life distributions for products exposed to many damage-induced cycles. It proposes discrete life distribution models for products whose failures occur within several damaging cycles. The actual cycles for various applicable damaging stress profiles are converted into the equivalent (pseudo) cycles under a reference stress profile. When damage-induced cycles are estimated from user surveys, they may involve biases, as is the nature of most nontechnical users’ responses. This bias is minimized using an approach based on the Kullback-Leibler divergence method. The survey data and other evidence from similar products are then combined with the test data of the new product to estimate the parameters of the reliability model of the new product. The dissertation developed approaches to design reliability test specifications for a new product with unknown failure modes. The number of samples, stress levels, and the number of cycles for the accelerated life test are determined based on the manufacturer’s requirements, including the desired warranty time, the desired reliability with some confidence level at the warranty time, and the maximum number of samples. The actual use conditions (i.e., actual stress profiles and usage cycles) are grouped using clustering techniques. The centers of clusters are then used to design frequency-accelerated or stress-accelerated reliability tests. The application of the proposed reliability estimation approach and the test specification design approach is illustrated and used to validate the proposed algorithms using the simulated datasets for a hypothetical handheld electronic device with the failure mode of cracking caused by accidental drops. The proposed approaches can adequately estimate the reliability model and design test specifications for a wide range of consumer products. These approaches require reliability data about an existing product that is similar to the new product, however.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 CONNECTEDNESS EFFICIENCY ANALYSIS OF WEIGHTED U.S. FREIGHT RAILROAD NETWORKS(2022) Hamed, Majed; Ayyub, Bilal M.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Freight rail networks serve an essential role in transporting goods to accommodate marketdynamic demands and public needs, and subsequently network analyses of such systems become of importance for providing insights into enhancing transportation efficiency and resilience. This thesis develops and investigates a topological analysis model termed as connectedness efficiency, which is associated with the connectedness of a network’s nodes by its links and corresponding attributes. Analysis outcomes from such a model can be utilized for providing economical insights on the network’s performance. This model can be used to analyze network topologies without assigning weights to their nodes and links, or with weight assignments to nodes and links based on different attributes, such as volumes of goods handled at nodes, physical-length of links, commodity volume transported through links, and travel fuel cost through links. Such an analysis can be utilized for: (1) defining distinctions that may be employed for the assignment of node and link weights, (2) gaining understanding of node and link criticality, and (3) providing methods for objectively maintaining and enhancing network performance. This analysis informs decisions to be considered by rail managers and executives in financial management, planning expansions, route changes, or preparations for potential node or link failures. A case study using an aggregate U.S. freight railroad network along with other example topologies is presented to examine different network attributes as well as their influence on connectedness efficiency and loss impacts of nodes and links.Item Bayesian Methodology for Reliability Growth Planning and Projection for Discrete-Use Systems Utilizing Multi-Source Data(2021) Nation, Paul John; Modarres, Mohammad; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research aims to present a Bayesian model for reliability growth projection and planning for discrete-use systems suitable for use throughout all stages of system development. Traditional discrete-use models for reliability growth utilize test data from individual test events at the current stage of development. They often neglect the inclusion of historical information from previous tests, testing similar systems, or eliciting expert opinion. Examining and using data attained from prior bench analyses, sub-system tests, or user trial events often fails to occur or is conducted poorly. Additionally, no current approach permits the probabilistic treatment of the initial system reliability at the commencement of the test program in conjunction with the management variables that may change throughout the execution of the test plan.This research aims to contribute to the literature in several ways. Firstly, a new Bayesian model is developed from first principles, which considers the uncertainty surrounding discrete-use systems under arbitrary corrective action regimes to address failure modes. This differs from current models that fail to address the randomized times that corrective actions to observed failure modes may be implemented depending on the selected management strategy. Some current models only utilize the first observed failure on test, meaning a significant loss of information transpires if subsequent failures are ignored. Additionally, the proposed strategy permits a probabilistic assessment of the test program, accounting for uncertainty in several management variables. The second contribution seeks to extend the Bayesian discrete-use system projection model by considering aspects of developmental, acceptance, and operational testing to formulate a holistic reliability growth plan framework that extends over the entire system lifecycle. The proposed approach considers the posterior distribution from each phase of reliability growth testing as the prior for the following growth test event. The same methodology is then employed using the posterior from the final phase of reliability growth testing as the prior for acceptance testing. It then follows that the acceptance testing posterior distribution forms the prior for subsequent operational testing through a Bayesian learning method. The approach reduces unrealistic and unattainable reliability demonstration testing that may result from a purely statistical analysis. The proposed methodology also permits planning for combined developmental and acceptance test activities within a financially constrained context. Finally, the research seeks to define an approach to effectively communicate developmental system reliability growth plans and risks to decision makers. Like many of their other specialist science peers, reliability professionals are fantastic communicators – with other reliability practitioners. However, when reliability professionals move beyond their world to make an impact, they often face the same challenge scientists from every discipline face – the difficulties of clearly communicating science to their audience.Item Knowing to Ask and Asking to Know: The Reciprocal Nature of Inquiry and Selectivity(2021) Gibbs, Hailey Margaret; Butler, Lucas P.; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Children are resourceful learners, capable of learning about the world both through hands-on experience and by engaging with other members of their communities. Questions play a particularly central role in children’s early learning, allowing them targeted, direct access to what others know. In this study, children aged 4-7 were presented with animations of puppet characters playing a Question Game in which one character reliably asks more efficient questions than the other. In three generalization trials, children were asked to extend their judgments of the characters’ questioning abilities to determine which character would be more reliable, which would be a better teacher, and which would be a more competent problem-solver. Children as young as 4 were able to identify the more efficient questioner and could generate their own overall assessment of a character’s questioning ability given previous experience with their use of strategy. Children did not generalize questioning strategy to reliability, but they did appear to view better questioners as broadly more knowledgeable and more competent. The extent to which children justified their choices by referencing relative information gain did not predict their identification of a better questioner in the generalization trials, though it did increase significantly with age and was significantly predicted by their scores in the Question Game. This suggests that, with age, children become more adept both at identifying better questions and in providing cogent explanations for their reasoning. Future work is needed to explore older age groups and develop strategies to help children make direct connections between questioning strategy and relative information gain.Item RELIABILITY-BASED MODELING FOR MISSOURI RIVER DAM SYSTEM(2020) ma, zihui; Baecher, Gregory B; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recently, the dam failure types shift from traditional causes (such as system nature of incidents) to operational risk. These failures occur because of unforeseen combinations of usual conditions. Since the operational components have complex internal and external interactions, we take them into an integrated system. Moreover, the Monte-Carlo simulation method was applied to develop a reliability-based model to study the system performance. Our approach incorporates different sources of uncertainty, including failure probability of components such as turbines and spillway gate facilities. This model allowed us to evaluate the reliability and availability of the system. The system reliability analysis helps us understand the relationship between failure modes and safety decisions made. In further, the model also allows experimenting on operational strategies as well as maintenance guidelines. This thesis presents the framework we have developed and illustrated the results and analysis of our application in the Missouri River mainstem reservoir system. In addition, four scenarios which Corps engineers are going to consider, have been applied to explicit the impacts of modeling system with different maintenance strategies. Besides, we used the stochastic time-series inflow instead of our historical data to evaluate the system performance.Item DEEP ADVERSARIAL APPROACHES IN RELIABILITY(2020) Verstraete, David Benjamin; Modarres, Mohammad; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Reliability engineering has long been proposed with the problem of predicting failures using all available data. As modeling techniques have become more sophisticated, so too have the data sources from which reliability engineers can draw conclusions. The Internet of Things (IoT) and cheap sensing technologies have ushered in a new expansive set of multi-dimensional big machinery data in which previous reliability engineering modeling techniques remain ill-equipped to handle. Therefore, the objective of this dissertation is to develop and advance reliability engineering research by proposing four comprehensive deep learning methodologies to handle these big machinery data sets. In this dissertation, a supervised fault diagnostic deep learning approach with applications to the rolling element bearings incorporating a deep convolutional neural network on time-frequency images was developed. A semi-supervised generative adversarial networks-based approach to fault diagnostics using the same time-frequency images was proposed. The time-frequency images were used again in the development of an unsupervised generative adversarial network-based methodology for fault diagnostics. Finally, to advance the studies of remaining useful life prediction, a mathematical formulation and subsequent methodology to combine variational autoencoders and generative adversarial networks within a state-space modeling framework to achieve both unsupervised and semi-supervised remaining useful life estimation was proposed. All four proposed contributions showed state of the art results for both fault diagnostics and remaining useful life estimation. While this research utilized publicly available rolling element bearings and turbofan engine data sets, this research is intended to be a comprehensive approach such that it can be applied to a data set of the engineer’s chosen field. This research highlights the potential for deep learning-based approaches within reliability engineering problems.