Theses and Dissertations from UMD

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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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Subject: search.filters.subject.Human Reliability Analysis

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Now showing 1 - 5 of 5
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    Improving the Foundational Knowledge of Dependency in Human Reliability Analysis
    (2023) Paglioni, Vincent Philip; Groth, Katrina M; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Human reliability analysis (HRA) is the field tasked with understanding, modeling, and quantifying the human contribution to the reliability of complex engineering systems. Human machine teams (HMTs, the groups of operators and human-system interface technologies that together control a system) currently contribute to over 60% of industrial accidents and will continue to servean important operational role in complex engineering systems. As a result, it is critical to develop robust methods for characterizing HMT performance and reliability. One of the factors limiting the technical basis of HRA is the treatment of dependency, how task performances and influencing factors are causally connected. Currently, HRA does not have a sound framework for conceptualizing, modeling, or quantifying dependency. The concept of dependency is poorly defined, the modeling is lacks a causal basis, and the quantification of dependency is unsupported by literature or data. This research closes these gaps in the foundations of HRA dependency by enforcing a rigorous, quantitative causal basis for the conceptualization and modeling of dependency. First, this research addresses the definitional and conceptual foundations of HRA dependency to provide a consistent technical basis for the field. This work proposes a single, complete, and appropriate definition for the general concept of dependency; one that is rooted in causality. This research also provides definitions for dependency-related concepts from multiple fields including probability, statistics, and set theory. The definitional basis laid out by this work standardizes the foundations of the field and promotes the ability to more easily translate between previously disparate HRA methods. Second, this work develops the causal structure of dependency in HRA. Whereas current methods for dependency modeling in HRA focus on correlational attributes, this method recognizes that causality, not correlation, is the driving mechanism of dependency. This work identifies six distinct relationship archetypes (idioms) that describe the general dependency relationships possible between HRA variables. Furthermore, this work creates the graphical structures that describe the idioms using Bayesian Networks (BNs) as the modeling architecture. The task/function-level idiom structures created in this work provide robust, traceable models of dependency relationships that can be used to both build HRA models and decompose full models into more understandable pieces. Third, this work develops the methodology to build and quantify causal, formative dependency BN HRA models using the idiom structures and HRA data. Whereas many HRA methods rely on expert elicitation alone for assigning probabilities, this methodology quantifies the network directly from HRA data. The methodology developed in this work produces a full, causal, formative dependency scenario model without requiring expert elicitation of probabilities. This methodology is implemented to build and quantify a scenario model using real HRA data collected from operator crews working in a full-scope nuclear reactor simulator, which shows both that causal dependency can be modeled and quantified, and that the methodology is traceable and useful. Finally, this work develops a set of recommendations for the collection, storage, and use of HRA data, and for the implementation of this methodology within mature HRA frameworks. This dissertation will improve our knowledge of, and ability to model, dependency in human reliability.
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    AN INVESTIGATION ON THE AFFECT OF EXTERNAL CONDITIONS ON THE RELIABILITY OF AIRCRAFT INSPECTIONS
    (2017) Barrett, Adam David; Modarres, Mohammad; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The objective of this study is to develop an understanding of how external variables commonly encountered during an inspection affect an inspection systems detection capability. A probability of detection study was performed using representative structural samples, attached to a simulated naval flight asset. For the execution of these tests, common nondestructive testing equipment was utilized by multiple inspectors. For each test inspectors, test samples (with imbedded damage) and inspection locations around the test bed were varied to better simulate field inspection conditions. An understanding of how these variables affect inspection performance will give maintainers, designers, and planners a more realistic idea of what damage can be detected and quantified in field inspection conditions.
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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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    MODELING AND SIMULATION OF OPERATOR KNOWLEDGE-BASED BEHAVIOR
    (2013) Li, Yuandan; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Many accidents are attributed to human errors. Abundant evidences could be found in major accidents in petro-chemical, nuclear, aviation, and other industries. In the nuclear power industry, safe operation heavily relies on the operators' interaction with plant systems. For example, Three Mile Island accident was exacerbated by the operators' misdiagnosis of the situation, which led to the termination of the plant's automatic protection system that could have prevented meltdown of the reactor core. Hence, human Reliability Analysis (HRA) is an important ingredient of Probabilistic Risk Assessment (PRA), particularly in the nuclear industry. HRA aims to predict possible human errors, identify "error forcing contexts", and assess error probabilities. Despite advances in HRA discipline over the past two decades, virtually all existing methods lack a causal model and few leverage the theoretical and empirical insights for error prediction in a systematic and formal way. One approach that has attempted to address this major shortcoming is IDAC crew simulation model of ADS-IDAC dynamic PRA platform. Through the interactions between an IDAC crew model and a pressurizer water reactor plant model, ADS-IDAC dynamically simulates the operators' cognitive activities and actions in an accident condition. The goal of proposed research is to introduce an advanced reasoning capability and structured knowledge representation to enhance the realism and predictive power of in the IDAC model for situations where crew behaviors are governed by both the Emergency Operating Procedure (EOP) and their knowledge of the plant. This is achieved by: 1) Developing and implementing a cognitive architecture to simulate operators' understanding of accident conditions and plant response, their reasoning processes and knowledge utilization to make a diagnosis. A reasoning module has been added to the individual operator model within IDAC model to mimic operators knowledge-based reasoning processes; 2) Developing and applying a comprehensive set of Performance Shaping Factors (PSF) to model the impacts of situational and cognitive factors on operators' behaviors. The effects and interdependencies of PSFs are incorporated the reasoning module; and 3) Performing a calibration and validation of the model predictions by comparing the simulation results with results of a number of plant-crew simulator exercises.
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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.