Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
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 give 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 STATISTICAL DATA FUSION WITH DENSITY RATIO MODEL AND EXTENSION TO RESIDUAL COHERENCE(2024) Zhang, Xuze; Kedem, Benjamin; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Nowadays, the statistical analysis of data from diverse sources has become more prevalent. The Density Ratio Model (DRM) is one of the methods for fusing and analyzing such data. The population distributions of different samples can be estimated basedon fused data, which leads to more precise estimates of the probability distributions. These probability distributions are related by assuming the ratios of their probability density functions (PDFs) follow a parametric form. In the previous works, this parametric form is assumed to be uniform for all ratios. In Chapter 1, an extension is made to allow this parametric form to vary for different ratios. Two methods of determining the parametric form for each ratio are developed based on asymptotic test and Akaike Information Criterion (AIC). This extended DRM is applied to Radon concentration and Pertussis rates to demonstrate the use of this extension in univariate case and multivariate case, respectively. The above analysis is made possible when data in each sample are independent and identically distributed (IID). However, in many cases, statistical analysis is entailed for time series in which data appear to be sequentially dependent. In Chapter 2, an extension is made for DRM to account for weakly dependent data, which allows us to investigate the structure of multiple time series on the strength of each other. It is shown that the IID assumption can be replaced by proper stationarity, mixing and moment conditions. This extended DRM is applied to the analysis of air quality data which are recorded in chronological order. As mentioned above, DRM is suitable for the situation where we investigate a single time series based on multiple alternative ones. These time series are assumed to be mutually independent. However, in time series analysis, it is often of interest to detect linear and nonlinear dependence between different time series. In such dependent scenario, coherence is a common tool to measure the linear dependence between two time series, and residual coherence is used to detect a possible quadratic relationship. In Chapter 3, we extend the notion of residual coherence and develop statistical tests for detecting linear and nonlinear associations between time series. These tests are applied to the analysis of brain functional connectivity data.Item DEVELOPING HYBRID PHM MODELS FOR PIPELINE PITTING CORROSION, CONSIDERING DIFFERENT TYPES OF UNCERTAINTY AND CHANGES IN OPERATIONAL CONDITIONS(2019) Heidarydashtarjandi, Roohollah; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Pipelines are the most efficient and reliable way to transfer oil and gas in large quantities. Pipeline infrastructures represent a high capital investment and, if they fail, a source of environmental hazards and a potential threat to life. Among different pipeline failure mechanisms, pitting corrosion is of most concern because of the high growth rate of pits. In this dissertation two hybrid prognostics and health management (PHM) models are developed to evaluate degradation level of piggable pipelines, due to internal pitting corrosion. These models are able to incorporate multiple sensors data and physics of failure (POF) knowledge of internal pitting corrosion process. This dissertation covers both cases when in some pipeline's segments the pit density is low and in some segments it is high. In addition, it takes into account four types of uncertainty, including epistemic uncertainty, variability in the temporal aspects, spatial heterogeneity, and inspection errors. For a pipeline segment with a low pit density, a hybrid defect-based algorithm is developed to estimate probability distribution of maximum depth of each individual pit on that segment. This algorithm considers change in operational condition in internal pitting corrosion degradation modeling for the first time. In this way a two-phase similarity-based data fusion algorithm is developed to fuse POF knowledge, in-line inspection (ILI) and online inspection (OLI) data. In the first phase, a hierarchical Bayesian method based on a non-homogeneous gamma process is used to fuse POF knowledge and in-line inspection (ILI) data on multiple pits, and augmented particle filtering is used to fuse POF knowledge and online inspection (OLI) data of an active reference pit. The results are used to define a similarity index between each ILI pit and the OLI pit. In the second phase, this similarity index is used to generate dummy observations of depth for each ILI pit, based on the inspection data of the OLI pit. Those dummy observations are used in augmented particle filtering to estimate the remaining useful life (RUL) of that segment after the change in operational conditions when there is no new ILI data. For a pipeline segment with a high pit density, a hybrid population-based algorithm is developed to estimate the probability density function of maximum depth of the pit population on that segment. This algorithm eliminates the need of matching procedure that is computationally expensive and prone to error when the pit density is high. In this algorithm three types of measurement uncertainty including sizing error, probability of detection (POD), and probability of false call (POFC) are taken into account. In addition, initiation of new pits between the last ILI and a prediction time is modeled by using a homogeneous Poisson process. The non-linearity of the pitting corrosion process and the POF knowledge of this process is modeled by using a non-homogeneous gamma process. The estimation of these two algorithms are used in a series system to estimate the reliability of a long pipeline with multiple segments, when in some segments the pit density is low and in some segments it is high. The output of this research can be used to find the optimal maintenance action and time for each segment and the optimal next ILI time for the whole pipeline that eventually decreases the cost of unpredicted failures and unnecessary maintenance activities.