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 Systematic Integration of PHM and PRA (SIPPRA) for Risk and Reliability Analysis of Complex Engineering Systems(2021) Moradi, Ramin; Groth, Katrina; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Complex Engineering Systems (CES) such as power plants, process plants, and manufacturing plants have numerous, interrelated, and heterogeneous subsystems with different characteristics and risk and reliability analysis requirements. With the advancements in sensing and computing technology, abundant monitoring data is being collected. This is a rich source of information for more accurate assessment and management of these systems. The current risk and reliability analysis approaches and practices are inadequate in incorporating various sources of information, providing a system-level perspective, and performing a dynamic assessment of the operation condition and operation risk of CES. In this dissertation, this challenge is addressed by integrating techniques and models from two of the major subfields of reliability engineering: Probabilistic Risk Assessment (PRA) and Prognostics and Health Management (PHM). PRA is very effective at modeling complex hardware systems, and approaches have been designed to incorporate the risks introduced by humans, software, organizational, and other contributors into quantitative risk assessments. However, PRA has largely been used as a static technology mainly used for regulation. On the other hand, PHM has developed powerful new algorithms for understanding and predicting mechanical and electrical device health to support maintenance. Yet, PHM lacks the system-level perspective, relies heavily on operation data, and its outcomes are not risk-informed. I propose a novel framework at the intersection of PHM and PRA which provides a forward-looking, model- and data-driven analysis paradigm for assessing and predicting the operation risk and condition of CES. I operationalize this framework by developing two mathematical architectures and applying them to real-world systems. The first architecture is focused on enabling online system-level condition monitoring. The second architecture improves upon the first and realizes the objectives of using various sources of information and monitoring operation condition together with operational risk.Item A General Cause Based Methodology for Analysis of Dependent Failures in System Risk and Reliability Assessments(2013) O'Connor, Andrew N.; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Traditional parametric Common Cause Failure (CCF) models quantify the soft dependencies between component failures through the use of empirical ratio relationships. Furthermore CCF modeling has been essentially restricted to identical components in redundant formations. While this has been advantageous in allowing the prediction of system reliability with little or no data, it has been prohibitive in other applications such as modeling the characteristics of a system design or including the characteristics of failure when assessing the risk significance of a failure or degraded performance event (known as an event assessment). This dissertation extends the traditional definition of CCF to model soft dependencies between like and non-like components. It does this through the explicit modeling of soft dependencies between systems (coupling factors) such as sharing a maintenance team or sharing a manufacturer. By modeling the soft dependencies explicitly these relationships can be individually quantified based on the specific design of the system and allows for more accurate event assessment given knowledge of the failure cause. Since the most data informed model in use is the Alpha Factor Model (AFM), it has been used as the baseline for the proposed solutions. This dissertation analyzes the US Nuclear Regulatory Commission's Common Cause Failure Database event data to determine the suitability of the data and failure taxonomy for use in the proposed cause-based models. Recognizing that CCF events are characterized by full or partial presence of "root cause" and "coupling factor" a refined failure taxonomy is proposed which provides a direct link between the failure cause category and the coupling factors. This dissertation proposes two CCF models (a) Partial Alpha Factor Model (PAFM) that accounts for the relevant coupling factors based on system design and provide event assessment with knowledge of the failure cause, and (b)General Dependency Model (GDM),which uses Bayesian Network to model the soft dependencies between components. This is done through the introduction of three parameters for each failure cause that relate to component fragility, failure cause rate, and failure cause propagation probability.Item A Bayesian Approach to Sensor Placement and System Health Monitoring(2012) Pourali, Masoud; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)System health monitoring and sensor placement are areas of great technical and scientific interest. Prognostics and health management of a complex system require multiple sensors to extract required information from the sensed environment, because no single sensor can obtain all the required information reliably at all times. The increasing costs of aging systems and infrastructures have become a major concern, and system health monitoring techniques can ensure increased safety and reliability of these systems. Similar concerns also exist for newly designed systems. The main objectives of this research were: (1) to find an effective way for optimal functional sensor placement under uncertainty, and (2) to develop a system health monitoring approach with both prognostic and diagnostic capabilities with limited and uncertain information sensing and monitoring points. This dissertation provides a functional/information --based sensor placement methodology for monitoring the health (state of reliability) of a system and utilizes it in a new system health monitoring approach. The developed sensor placement method is based on Bayesian techniques and is capable of functional sensor placement under uncertainty. It takes into account the uncertainty inherent in characteristics of sensors as well. It uses Bayesian networks for modeling and reasoning the uncertainties as well as for updating the state of knowledge for unknowns of interest and utilizes information metrics for sensor placement based on the amount of information each possible sensor placement scenario provides. A new system health monitoring methodology is also developed which is: (1) capable of assessing current state of a system's health and can predict the remaining life of the system (prognosis), and (2) through appropriate data processing and interpretation can point to elements of the system that have or are likely to cause system failure or degradation (diagnosis). It can also be set up as a dynamic monitoring system such that through consecutive time steps, the system sensors perform observations and send data to the Bayesian network for continuous health assessment. The proposed methodology is designed to answer important questions such as how to infer the health of a system based on limited number of monitoring points at certain subsystems (upward propagation); how to infer the health of a subsystem based on knowledge of the health of the main system (downward propagation); and how to infer the health of a subsystem based on knowledge of the health of other subsystems (distributed propagation).