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

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Subject: search.filters.subject.Bayesian Belief Networks

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Now showing 1 - 3 of 3
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    A MODEL-BASED HUMAN RELIABILITY ANALYSIS METHODOLOGY (PHOENIX METHOD)
    (2013) Ekanem, Nsimah J.; MOSLEH, ALI; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Despite the advances made so far in developing human reliability analysis (HRA) methods, many issues still exist. Most notable are; the lack of an explicit causal model that incorporates relevant psychological and cognitive theories in its core human performance model, inability to explicitly model interdependencies between human failure events (HFEs) and influencing factors on human performance, lack of consistency, traceability and reproducibility in HRA analysis. These issues amongst others have contributed to the variability in results seen in the application of different HRA methods and even in cases where the same method is applied by different analysts. In an attempt to address these issues, a framework for a model-based HRA methodology has been recently proposed which incorporates strong elements of current HRA good practices, leverages lessons learned from empirical studies and the best features of existing and emerging HRA methods. This research completely develops this methodology which is aimed at enabling a more credible, consistent, and accurate qualitative and quantitative HRA analysis. The complete qualitative analysis procedure (including a hierarchical performance influencing factor set) and a causal model using Bayesian Belief network (BBN) have been developed to explicitly model the influence and dependencies among HFEs and the different factors that influence human performance. This model has the flexibility to be modified for interfacing with existing methods like Standard-Plant-Analysis-Risk-HRA-method. Also, the quantitative analysis procedure has been developed, incorporating a methodology for a cause-based explicit treatment of dependencies among HFEs, which has not been adequately addressed by any other HRA method. As part of this research, information has been gathered from sources (including other HRA methods, NPP operating experience, expert estimates), analyzed and aggregated to provide estimates for the model parameters needed for quantification. While the specific instance of this HRA method is used in nuclear power plants, the methodology itself is generic and can be applied in other environments.
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    A DATA-INFORMED MODEL OF PERFORMANCE SHAPING FACTORS FOR USE IN HUMAN RELIABILITY ANALYSIS
    (2009) Groth, Katrina M.; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Many Human Reliability Analysis (HRA) models use Performance Shaping Factors (PSFs) to incorporate human elements into system safety analysis and to calculate the Human Error Probability (HEP). Current HRA methods rely on different sets of PSFs that range from a few to over 50 PSFs, with varying degrees of interdependency among the PSFs. This interdependency is observed in almost every set of PSFs, yet few HRA methods offer a way to account for dependency among PSFs. The methods that do address interdependencies generally do so by varying different multipliers in linear or log-linear formulas. These relationships could be more accurately represented in a causal model of PSF interdependencies. This dissertation introduces a methodology to produce a Bayesian Belief Network (BBN) of interactions among PSFs. The dissertation also presents a set of fundamental guidelines for the creation of a PSF set, a hierarchy of PSFs developed specifically for causal modeling, and a set of models developed using currently available data. The models, methodology, and PSF set were developed using nuclear power plant data available from two sources: information collected by the University of Maryland for the Information-Decision-Action model [1] and data from the Human Events Repository and Analysis (HERA) database [2] , currently under development by the United States Nuclear Regulatory Commission. Creation of the methodology, the PSF hierarchy, and the models was an iterative process that incorporated information from available data, current HRA methods, and expert workshops. The fundamental guidelines are the result of insights gathered during the process of developing the methodology; these guidelines were applied to the final PSF hierarchy. The PSF hierarchy reduces overlap among the PSFs so that patterns of dependency observed in the data can be attribute to PSF interdependencies instead of overlapping definitions. It includes multiple levels of generic PSFs that can be expanded or collapsed for different applications. The model development methodology employs correlation and factor analysis to systematically collapse the PSF hierarchy and form the model structure. Factor analysis is also used to identify Error Contexts (ECs) – specific PSF combinations that together produce an increased probability of human error (versus the net effect of the PSFs acting alone). Three models were created to demonstrate how the methodology can be used provide different types of data-informed insights. By employing Bayes' Theorem, the resulting model can be used to replace linear calculations for HEPs used in Probabilistic Risk Assessment. When additional data becomes available, the methodology can be used to produce updated causal models to further refine HEP values.
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    Development and Validation of Methodology for Fix Effectiveness Projection During Product Development
    (2009) Brown, Stephen Mark; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    One of the challenges that design and reliability engineers face is how to accurately project fix effectiveness during reliability planning of a product development project. All reliability projection methods currently in use require estimates of the fix effectiveness factors (FEF) in their mathematical formulation. Obviously, required test results from multiple test phases are unavailable at the onset of a project and therefore practice is to rely on engineers' subjective assessment FEFs. Such estimates are often inaccurate and mostly optimist, resulting in potentiality significant project risks in the form of delays, additional development costs, and costs associated with field failures, returns, and market position. This dissertation provides a methodology that significantly improves the accuracy of FEF estimates and also the resulting reliability metrics such as projected failures rates and MTBFs. The methodology identifies key "performance shaping factors" (PSF) that enhances or impedes an engineer's ability to "fix" a problem, and puts that information into a "causal model" via Bayesian Belief Networks (BBN) to predict FEFs. Tests and confirmation of the methodology for various products and diverse industries show a systematic error reduction in FEF estimates over the current use of unstructured subjective estimates. A second major contribution of the research is an investigation of the effect of interdependencies among various FEFs in projecting the reliability of the same product or several different products by the same organization. Independence is currently assumed by all reliability projection methods. The research (i) shows that FEFs are indeed dependent, (ii) provides a composite BBN model showing the level of dependency among two different fix activities, and (iii) quantifies the impact that fix effectiveness factors have on MTBF projections. The research therefore presents an important augmentation to the current IEC standard for reliability growth, Crow-AMSAA model, showing how to include dependent FEFs in the calculation of failure intensity.