A. James Clark School of Engineering
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item 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.Item Development of Approaches to Common Cause Dependencies with Applications to Multi-Unit Nuclear Power Plant(2018) Zhou, Taotao; Modarres, Mohammad; Droguett, Enrique López; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The term “common cause dependencies” encompasses the possible mechanisms that directly compromise components performances and ultimately cause degradation or failure of multiple components, referred to as common cause failure (CCF) events. The CCF events have been a major contributor to the risk posed by the nuclear power plants and considerable research efforts have been devoted to model the impacts of CCF based on historical observations and engineering judgment, referred to as CCF models. However, most current probabilistic risk assessment (PRA) studies are restricted to single reactor units and could not appropriately consider the common cause dependencies across reactor units. Recently, the common cause dependencies across reactor units have attracted a lot of attention, especially following the 2011 Fukushima accident in Japan that involved multiple reactor unit damages and radioactive source term releases. To gain an accurate view of a site's risk profile, a site-based risk metric representing the entire site rather than single reactor unit should be considered and evaluated through a multi-unit PRA (MUPRA). However, the multi-unit risk is neither formally nor adequately addressed in either the regulatory or the commercial nuclear environments and there are still gaps in the PRA methods to model such multi-unit events. In particular, external events, especially seismic events, are expected to be very important in the assessment of risks related to multi-unit nuclear plant sites. The objective of this dissertation is to develop three inter-related approaches to address important issues in both external events and internal events in the MUPRA. 1) Develop a general MUPRA framework to identify and characterize the multi-unit events, and ultimately to assess the risk profile of multi-unit sites. 2) Develop an improved approach to seismic MUPRA through identifying and addressing the issues in the current methods for seismic dependency modeling. The proposed approach can also be extended to address other external events involved in the MUPRA. 3) Develop a novel CCF model for components undergoing age-related degradation by superimposing the maintenance impacts on the component degradation evolutions inferred from condition monitoring data. This approach advances the state-of-the-art CCF analysis in general and assists in the studies of internal events of the MUPRA.Item Risk Management for Enterprise Resource Planning System Implementations in Project-Based Firms(2010) ZENG, YAJUN; Skibniewski, Miroslaw J.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Enterprise Resource Planning (ERP) systems have been regarded as one of the most important information technology developments in the past decades. While ERP systems provide the potential to bring substantial benefits, their implementations are characterized with large capital outlay, long duration, and high risks of failure including implementation process failure and system usage failure. As a result, the adoption of ERP systems in project-based firms has been lagged behind lots of companies in many other industries. In order to ensure the success of ERP system implementations in project-based firms, sound risk management is the key. The overall objective of this research is to identify the risks in ERP system implementations within project-based firms and develop a new approach to analyze these risks and quantitatively assess their impacts on ERP system implementation failure. At first, the research describes ERP systems in conjunction with the nature and working practices of project-based firms and current status and issues related to ERP adoption in such firms, and thus analyzes the causes for their relatively low ERP adoption and states the research problems and objectives. Accordingly, a conceptual research framework is presented, and the procedures and research methods are outlined. Secondly, based on the risk factors regarding generic ERP projects in extant literature, the research comprehensively identifies the risk factors of ERP system implementation within project-based firms. These risk factors are classified into different categories, qualitatively described and analyzed, and used to establish a risk taxonomy. Thirdly, an approach is developed based on fault tree analysis to decompose ERP systems failure and assess the relationships between ERP component failures and system usage failure, both qualitatively and quantitatively. The principles and processes of this approach and related fault tree analysis methods and techniques are presented in the context of ERP projects. Fourthly, certain practical strategies are proposed to manage the risks of ERP system implementations. The proposed risk assessment approach and management strategies together with the comprehensive list of identified risk factors not only contribute to the body of knowledge of information system risk management, but also can be used as an effective tool by practitioners to actively analyze, assess, and manage the risks of ERP system implementations within project-based firms.Item Engineering-Based Probabilistic Risk Assessment for Food Safety with Application to Escherichia coli O157:H7 Contamination in Cheese(2006-04-26) Fretz, Kristin; Modarres, Mohammad; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A new methodology is introduced in which engineering-based tools and techniques are adapted to quantitative microbial risk assessment (QMRA) in order to offer a more systematic solution to food safety problems. By integrating available microbial data and adapted engineering techniques within the traditional QMRA framework, this new methodology addresses some of the deficiencies of traditional approaches. Through the use of a hierarchical structure, the system is decomposed into its most basic elements so that the interrelationships and interdependences of these basic elements are captured. This hierarchical structure also identifies variability throughout the process, resulting in a risk model in which multiple scenarios can be analyzed. In addition, the engineering approach adapts methods for characterizing and propagating uncertainties. Unlike the traditional approaches in food safety, the engineering-based methodology relies on mathematical models; the uncertainties about these models (both aleatory and epistemic), as well as the uncertainties about the model parameters, are formally quantified and properly considered. This separation and characterization of uncertainties results in a more powerful risk model, so that assessments can be made as to whether additional information or changes to the physical system will reduce the total uncertainty. Finally, this research characterizes the validity of the various dose-response models. Comparison of actual outbreak observations to model predictions lends credibility and assesses uncertainty of the developed dose-response models. Thus, the results of the risk model can be used both as an absolute assessment of risk and as a relative measurement of mitigation and control strategies. As a case study, the engineering-based methodology is applied to the problem of Escherichia coli O157:H7 contamination in cheese. While it has been assumed that pathogenic microorganisms in raw milk die during cheese-making, several studies on the survival of E. coli O157:H7 in cheese have demonstrated growth during cheese manufacturing. Furthermore, E. coli O157:H7 has been linked to several outbreaks involving cheese, thereby establishing the need to investigate this route of transmission. The successful application of the engineering-based approach to the problem of E. coli O157:H7 contamination in cheese suggests that this new methodology can be applied to other food safety problems.Item INTEGRATING SOFTWARE BEHAVIOR INTO DYNAMIC PROBABILISTIC RISK ASSESSMENT(2005-12-21) Zhu, Dongfeng; Smidts, Carol; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Software plays an increasingly important role in modern safety-critical systems. Although research has been done to integrate software into the classical Probability Risk Assessment (PRA) framework, current PRA practice overwhelmingly neglects the contribution of software to system risk. The objective of this research is to develop a methodology to integrate software contributions in the Dynamic Probabilistic Risk Assessment (DPRA) environment. DPRA is considered to be the next generation of PRA techniques. It is a set of methods and techniques in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. DPRA allows consideration of dynamic interactions of system elements and physical variables. The fact remains, however, that modeling software for use in the DPRA framework is also quite complex and very little has been done to address the question directly and comprehensively. This dissertation describes a framework and a set of techniques to extend the DPRA approach to allow consideration of the software contributions on system risk. The framework includes a software representation, an approach to incorporate the software representation into the DPRA environment SimPRA, and an experimental demonstration of the methodology. This dissertation also proposes a framework to simulate the multi-level objects in the simulation based DPRA environment. This is a new methodology to address the state explosion problem. The results indicate that the DPRA simulation performance is improved using the new approach. The entire methodology is implemented in the SimPRA software. An easy to use tool is developed to help the analyst to develop the software model. This study is the first systematic effort to integrate software risk contributions into the dynamic PRA environment.