Mechanical Engineering
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Item Advanced methodologies for reliability-based design optimization and structural health prognostics(2010) Wang, Pingfeng; Youn, Byeng Dong; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Failures of engineered systems can lead to significant economic and societal losses. To minimize the losses, reliability must be ensured throughout the system's lifecycle in the presence of manufacturing variability and uncertain operational conditions. Many reliability-based design optimization (RBDO) techniques have been developed to ensure high reliability of engineered system design under manufacturing variability. Schedule-based maintenance, although expensive, has been a popular method to maintain highly reliable engineered systems under uncertain operational conditions. However, so far there is no cost-effective and systematic approach to ensure high reliability of engineered systems throughout their lifecycles while accounting for both the manufacturing variability and uncertain operational conditions. Inspired by an intrinsic ability of systems in ecology, economics, and other fields that is able to proactively adjust their functioning to avoid potential system failures, this dissertation attempts to adaptively manage engineered system reliability during its lifecycle by advancing two essential and co-related research areas: system RBDO and prognostics and health management (PHM). System RBDO ensures high reliability of an engineered system in the early design stage, whereas capitalizing on PHM technology enables the system to proactively avoid failures in its operation stage. Extensive literature reviews in these areas have identified four key research issues: (1) how system failure modes and their interactions can be analyzed in a statistical sense; (2) how limited data for input manufacturing variability can be used for RBDO; (3) how sensor networks can be designed to effectively monitor system health degradation under highly uncertain operational conditions; and (4) how accurate and timely remaining useful lives of systems can be predicted under highly uncertain operational conditions. To properly address these key research issues, this dissertation lays out four research thrusts in the following chapters: Chapter 3 - Complementary Intersection Method for System Reliability Analysis, Chapter 4 - Bayesian Approach to RBDO, Chapter 5 - Sensing Function Design for Structural Health Prognostics, and Chapter 6 - A Generic Framework for Structural Health Prognostics. Multiple engineering case studies are presented to demonstrate the feasibility and effectiveness of the proposed RBDO and PHM techniques for ensuring and improving the reliability of engineered systems within their lifecycles.Item 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.Item Simulation and Optimization of Production Control for Lean Manufacturing Transition(2008-08-06) Gahagan, Sean Michael; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Lean manufacturing is an operations management philosophy that advocates eliminating waste, including work-in-process (WIP) inventory. A common mechanism for controlling WIP is "pull" production control, which limits the amount of WIP at each stage. The process of transforming a system from push production control to pull is not well understood or studied. This dissertation explores the events of a production control transition, quantifies its costs and develops techniques to minimize them. Simulation models of systems undergoing transition from push to pull are used to study this transient behavior. The transition of a single stage system is modeled. An objective function is introduced that defines transition cost in terms of the holding cost of orders in backlog and material in inventory. It incorporates two techniques for mitigating cost: temporarily deferring orders and adding extra capacity. It is shown that, except when backlog costs are high, it is better to transform the system quickly. It is also demonstrated that simulation based optimization is a viable tool to find the optimal transition strategy. Transition of a two-stage system is also modeled. The performance of two simple multi-stage transition strategies is measured. In the first, all of the stages are transformed at the same time. In the second, they are transformed one at a time. It is shown that the latter strategy is superior. Other strategies are also discussed. A new modeling formalism, the Production Control Framework (PCF), is introduced to facilitate automated searches for transition strategies in more complex systems. It is a hierarchical description of a manufacturing system built on a novel extension of the classic queue server model, which can express production control policy parametrically. The PCF is implemented in the form of a software template and its utility is shown as it is used to model and then find the optimal production control policy for a five stage system. This work provides the first practical guidance and insight into the behavior and cost of Lean production control transition, and it lays the groundwork for the development of optimal transition strategies for even the most complex manufacturing systems.Item Development and Evaluation of Algorithms for Scheduling Two Unrelated Parallel Processors(2007-08-09) Leber, Dennis D; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Given a group of tasks and two non-identical processors with the ability to complete each task, how should the tasks be assigned to complete the group of tasks as quickly as possible? This thesis considers this unrelated parallel machine scheduling problem with the objective of minimizing the completion time of a group of tasks (the makespan) from the perspective of a local printed circuit board manufacturer. An analytical model representing the job dependent processing time for each manufacturing line is developed and actual job data supplied by the manufacturer is used for analysis. Two versions of a complete enumeration algorithm which identify the optimal assignment schedule are presented. Several classic assignment heuristics are considered with several additional heuristics developed as part of this work. The algorithms are evaluated and their performance compared for jobs built at the local manufacturing site. Finally, a cost-benefit tradeoff for the algorithms considered is presented.Item On the Theoretical Foundations and Principles of Organizational Safety Risk Analysis(2007-08-02) Mohaghegh-Ahmadabadi, Zahra; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research covers a targeted review of relevant theories and technical domains related to the incorporation of organizational factors into technological systems risk. In the absence of a comprehensive set of principles and modeling guidelines rooted in theory and empirical studies, all models look equally good, or equally poor, with very little basis to discriminate and build confidence. Therefore, this research focused on the possibility of improving the theoretical foundations and principles for the field of Organizational Safety Risk Analysis. Also, a process for adapting a hybrid modeling technique, in order to operationalize the theoretical organizational safety frameworks, is proposed. Candidate ingredients are techniques from Risk Assessment, Human Reliability, Social and Behavioral Science, Business Process Modeling, and Dynamic Modeling. Then, as a realization of aforementioned modeling principles, an organizational safety risk framework, named Socio-Technical Risk Analysis (SoTeRiA)is developed. The proposed framework considers the theoretical relation between organizational safety culture, organizational safety structure/practices, and organizational safety climate, with specific distinction between safety culture and safety climate. A systematic view of safety culture and safety climate fills an important gap in modeling complex system safety risk, and thus the proposed organizational safety risk theory describing the theoretical relation between two concepts to bridge this gap. In contrast to the current safety causal models which do not adequately consider the multilevel nature of the issue, the proposed multilevel causal model explicitly recognizes the relationships among constructs at multiple levels of analysis. Other contributions of this research are in implementing the proposed organizational safety framework in the aviation domain, particularly the airline maintenance system. The US Federal Aviation Administration (FAA), which has sponsored this research over the past three years, has recognized the issue of organizational factors as one of the most critical questions in the quest to achieve 80% reduction in aviation accidents. An example of the proposed hybrid modeling environment including an integration of System Dynamics (SD), Bayesian Belief Network (BBN), Event Sequence Diagram (ESD), and Fault Tree (FT), is also applied in order to demonstrate the value of hybrid frameworks. This hybrid technique integrates deterministic and probabilistic modeling perspectives, and provides a flexible risk management tool.Item Queueing Models and Assessment Tools for Improving Mass Dispensing and Vaccination Clinic Planning(2006-05-04) Treadwell, Mark; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)To react to an outbreak of a contagious disease that requires medication or vaccination, county health departments must set up and operate mass dispensing and vaccination centers, commonly known as points of dispensing (PODs), to treat residents who may be affected. Carefully planning these PODs before an event occurs is a difficult and important job. Simulation models can provide an accurate representation of resident flow through PODs, but are not convenient for public health professionals to access. Queueing theory provides a multitude of analytical models appropriate for various situations - so many models that it is often difficult to discern which model is correct for a particular circumstance. There are also some situations for which no models are available, particularly those involving batching and multiple servers. A complete set has been gathered of those models that are the most generalized, and hence useful for the widest range of applications. Where no appropriate model was available, modifications to the existing equations are proposed and tested. To implement this general queueing framework, software has been developed which can quickly generate planning models using steady-state queueing network approximations; these models use commonly available spreadsheet software to maximize accessibility for public health emergency planners. The planning models are validated against models created in several queueing software packages, along with simulation models automatically generated from the planning models. The number of stations and staff within a POD are not the only concerns that a public health emergency preparedness and response plan must address. A plan assessment tool is proposed, which can help planners ensure that their POD plans include all relevant information. A layout assessment tool is also developed, which endeavors to give planners suggestions on how to design PODs for maximum efficiency.Item A Structured Methodology For Identifying Performance Metrics And Monitoring Maintenance Effectiveness(2005-12-13) Amoedo, Maria Mercedes; Modarres, Mohammad; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Most current maintenance programs focus on achieving the main goals of maintenance operations: increasing mean time between failures, reducing time to repair and minimizing costs. Some researchers have focused on optimizing these variables. Detailed analyses have been conducted in the fields of equipment wellness, spares administration, planned maintenance and structured organization. Still, many organizations fail to fulfill today's ambitious objective of guaranteeing operations while achieving high reliability and maintaining safety. A comprehensive method of maintenance assessment that considers key factors and indicators that influence the main goals of maintenance is still sought after. This paper discusses a new approach to performance-based maintenance management. The objective is to determine an integrated reliability management system that provides a method of aligning maintenance operations with the business strategy and monitoring performance of key technical, human and organization goals over time.Item Risk and Economic Estimation of Inspection Policy of Periodically Tested Repairable Components(2005-08-02) Barroeta, Carlos Eduardo; Modarres, Mohammad; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This report presents a model to identify the optimal time between surveillance tests and overhaul frequency of components whose failures are detected upon inspection. The model is based on minimizing the total cost per unit time during the component renewal cycle. It considers the component availability assuming that the unit is "as old" after tests and repairs and "as new" after overhauls. The model takes into account costs associated with tests and maintenance, as well as potential losses related to unavailability. General conditions and a case study are discussed to evaluate the effect of costs, maintenance task durations, and the uncertainty of the reliability parameters on the optimal inspection policy of typical tested components. This report also discusses the advantage of the cost-based optimization versus the traditional approach based on maximal availability.Item Parameter Sensitivity Measures for Single Objective, Multi-Objective, and Feasibility Robust Design Optimization(2004-04-12) Gunawan, Subroto; Azarm, Shapour; Mechanical EngineeringUncontrollable variations are unavoidable in engineering design. If ignored, such variations can seriously deteriorate performance of an optimum design. Robust optimization is an approach that optimizes performance of a design and at the same time reduces its sensitivity to variations. The literature reports on numerous robust optimization techniques. In general, these techniques have three main shortcomings: (i) they presume probability distributions for parameter variations, which might be invalid, (ii) they limit parameter variations to a small (linear) range, and (iii) they use gradient information of objective/constraint functions. These shortcomings severely restrict applications of the techniques reported in the literature. The objective of this dissertation is to present a robust optimization method that addresses all of the above-mentioned shortcomings. In addition to being efficient, the robust optimization method of this dissertation is applicable to both single and multi-objective optimization problems. There are two steps in our robust optimization method. In the first step, the method measures robustness for a design alternative. The robustness measure is developed based on a concept that associated with each design alternative there is a sensitivity region in parameter variation space that determines how much variation a design alternative can absorb. The larger the size of this region, the more robust the design. The size of the sensitivity region is estimated by a hyper-sphere, using a worst-case approach. The radius of this hyper-sphere is obtained by solving an inner optimization problem. By comparing this radius to an actual range of parameter variations, it is determined whether or not a design alternative is robust. This comparison is added, in the second step, as an additional constraint to the original optimization problem. An optimization technique is then used to solve this problem and find a robust optimum design solution. As a demonstration, the robust optimization method is applied to numerous numerical and engineering examples. The results obtained are numerically analyzed and compared to nominal optimum designs, and to optimum designs obtained by a few well-known methods from the literature. The comparison study verifies that the solutions obtained by our method are indeed robust, and that the method is efficient.