Reliability Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/33173
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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 RISK ASSESSMENT AND MITIGATION OF TELECOM EQUIPMENT UNDER FREE AIR COOLING CONDITIONS(2012) Dai, Jun; Pecht, Michael G; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In recent years, about 40% of the total energy is devoted to the cooling infrastructures in data centers. One way to save energy is free air cooling (FAC), which utilizes the outside air as the primary cooling medium, instead of air conditioning, to reduce the energy consumption to cool the data centers. Despite the energy saving, the implementation of free air cooling will change the operating environment, which may adversely affect the performance and reliability of telecom equipment. This thesis reviews the challenges and risks posed by free air cooling. The increased temperature, uncontrolled humidity, and possible contamination may cause some failure mechanisms, e.g., Conductive anodic filament (CAF) and corrosion, to be more active. If the local temperatures of some hot spots go beyond their recommended operating conditions (RoC), the performances of the equipment may be affected. In this thesis, a methodology is proposed to identify the impact of free air cooling on telecom equipment performance. It uses the performance variations under traditional air condition (A/C) to create a baseline, and compares the performance variation under variable temperature and humidity representing FAC with the baseline. This method can help data centers determine an appropriate operating environment based on the service requirements, when FAC is implemented. In addition, a statics-based approach is also developed to identify the appropriate metric for the performance variations comparison. It is the first study focusing on the impact of FAC on the telecom equipment performance. This thesis also proposes a multi-stage (design, test, and operation) approach to mitigate the reliability risks of telecom equipment under free air cooling conditions. Specifically, a prognostics-based approach is proposed to mitigate the reliability risks at operation stage, and a case study is presented to show the implementation process. This approach needn't interrupt data center services and doesn't consume additional useful life of telecom equipment. It allows the implementation of FAC in data centers which were not originally designed for this cooling method.Item Application of Stochastic Reliability Modeling to Waterfall and Feature Driven Development Software Development Lifecycles(2011) Johnson, David Michael; Modarres, Mohammed; Smidts, Carol S; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There are many techniques for performing software reliability modeling. In the environment of software development some models use the stochastic nature of fault introduction and fault removal to predict reliability. This thesis research analyzes a stochastic approach to software reliability modeling and its performance on two distinct software development lifecycles. The derivation of the model is applied to each lifecycle. Contrasts between the lifecycles are shown. Actual data collected from industry projects illustrate the performance of the model to the lifecycle. Actual software development fault data is used in select phases of each lifecycle for comparisons with the model predicted fault data. Various enhancements to the model are presented and evaluated, including optimization of the parameters based on partial observations.Item Reliability Evaluation of Common-Cause Failures and Other Interdependencies in Large Reconfigurable Networks(2010) Guenzi, Giancarlo; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This work covers the impact of Interdependencies and CCFs in large repairable networks with possibility of "re-configuration" after a fault and the consequent disconnection of the faulted equipment. Typical networks with these characteristics are the Utilities, e.g. Power Transmission and Distribution Systems, Telecommunication Systems, Gas and Water Utilities, Wi Fi networks. The main issues of the research are: (a) Identification of the specific interdependencies and CCFs in large repairable networks, and (b)Evaluation of their impact on the reliability parameters (load nodes availability, etc.). The research has identified (1) the system and equipment failure modes that are relevant to interdependencies and CCF, and their subsequent effects, and (2) The hidden interdependencies and CCFs relevant to control, supervision and protection systems, and to the automatic change-over systems, that have no impact in normal operation, but that can cause relevant out-of-service when the above automatic systems are called to operate under and after fault conditions. Additionally methods were introduced to include interdependencies and CCFs in the reliability and availability models. The results of the research include a new generalized approach to model the repairable networks for reliability analysis, including Interdependencies/CCFs as a main contributor. The method covers Generalized models for Nodes, Branches and Load nodes; Interdependencies and CCFs on Networks / Components; System Interdependencies/CCFs; Functional Interdependencies/CCFs; Simultaneous and non-simultaneous Interdependencies/CCFs. As an example detailed Interdependency/CCFs analysis and generalized model of an important network structure (a "RING" with load nodes) has been analyzed in detail.Item AUTOMOTIVE DESIGN-TO LIFE-CYCLE CRITERIA FOR LOWERING WARRANTY COSTS AND IMPROVING OWNERSHIP EXPERIENCE THROUGH THE USE OF A NEW "BINARY DECISION MODEL" AND APPLICATION OF A "WARRANTY INDEX"(2009) Ireland, William C.; Christou, Aris; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Financial challenges facing the automotive sector require identification of new opportunities for quality improvement. A new Design-To Life-Cycle-Cost strategy is introduced that applies a unique "Binary Decision Logic Model" that classifies corrective action opportunity into Life-Cycle categories. The intended result is to lower a manufacture's warranty costs and improve ownership experience. This is done by setting Design-To goals in a Life-Cycle way for Reliability and Serviceability. The sample space for data to drive this change of process is found in an existing warranty system with data elements consisting of failure occurrence, failure symptom, mileage, part cost, and labor cost. One can investigate new factors, such as the "Warranty Index," that parses the corrective action in favor of lowering part costs or labor costs found in a typical service event. The data considers opportunities over mileage and time domains to improve vehicle quality over the Life-Cycle.