Reliability Engineering Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/33173

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    BAYESIAN BELIEF NETWORK AND FUZZY LOGIC ADAPTIVE MODELING OF DYNAMIC SYSTEM: EXTENSION AND COMPARISON
    (2010) CHENG, PING DANNY; MODARRES, MOHAMMAD; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The purpose of this thesis is to develop, expand, compare and contrast two methodologies, namely BBN and FLM, which are used in the modeling of the dynamics of physical system behavior and are instrumental in a better understanding on the POF. The paper begins with an introduction of the proposed approaches in the modeling of complex physical systems, followed by a quick literature review of FLM and BBN. This thesis uses an existing pump system [3] as a case study, where the resulting NPSHA data obtained from the applications of BBN and FLM are compared with the outputs derived from the implementation of a Mathematical Model. Based on these findings, discussions and analyses are made, including the identification of the respective strengths and weaknesses posed by the two methodologies. Last but not least, further extensions and improvements towards this research are discussed at the end of this paper.
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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.
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    Waking Effectiveness of Emergency Alerting Devices for the Hearing Able, Hard of Hearing, and Deaf Populations
    (2007-04-25) Ashley, Erin Mack; Milke, James; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The study presented measures the awakening effectiveness of a number of commercially available emergency alerting devices. Three groups of varying hearing levels were tested: hearing able, hard of hearing, and deaf. The devices evaluated are a typical audible smoke detector, a strobe light, and a bed shaker. The subjects were monitored for sleep stage during the single night tests and the emergency alerting devices were activated in Stage 2, Delta and REM stages of sleep. Results indicate that the audible smoke detector was most effective for the hearing able population and least effective for the deaf population. The recommended alternative to the audible smoke detector, the strobe, was the least effective device when measured against the total United States population. The vibratory tactile devices were most effective across all hearing categories and sleep stage. When the tactile signal of the bed shaker was modified to vibrate intermittently, all persons were effectively aroused. The research shows that the standard audible detector recommended for placement in all American homes is only effective in awakening those without hearing loss. The strobe is recommended by building and fire codes when hearing deficits are present but did not sufficiently awaken any population. Tactile devices can provide a sufficient means for awakening all populations regardless of hearing level, age or race
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    Analysis of Errors in Software Reliability Prediction Systems and Application of Model Uncertainty Theory to Provide Better Predictions
    (2006-07-14) Ghose, Susmita; Smidts, Carol; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Models are the medium by which we reflect and express our understanding of some aspect of reality, a particular unknown of interest. As it is virtually impossible to grasp any situation in its entire complexity, models are representations of reality that are always partial resulting in a state of uncertainty or error. However the question of model error from a pragmatic point of view is not one of accounting for the difference between models and reality at a fundamental level, as such difference always exists. Rather the question is whether the prediction or performance of the model is correct at some practically acceptable level, within the model's domain of application. Here lays the importance of assessing the impact of uncertainties about predictions of a model, modeling the error and trying to reduce the uncertainties associated as much as possible to provide better estimations. While the methods for assessing the impact of errors on the performance of a model and error modeling are well established in various scientific and engineering disciplines, to the best of our knowledge no substantial work has been done in the field of Software Reliability Modeling despite the fact that the inadequacy of the present state and techniques of software reliability estimation has been recognized by industry and government agencies. In summary, even though hundreds of software reliability models have been developed, the software reliability discipline is still struggling to establish a software reliability prediction framework. This work intends to improve the performance of software reliability models through error modeling. It analyzes the errors associated with a set of five software Reliability Prediction Systems (RePSs) and attempts to improve their prediction accuracy using a model uncertainty framework. In the process, this work also statistically validates the performances of the RePSs. It also provides a time and cost effective alternative to performing experiments that are required to assess the error form which is integral to the process of application of the model uncertainty framework.
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    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.
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    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.
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    A GUIDED SIMULATION METHODOLOGY FOR DYNAMIC PROBABILISTIC RISK ASSESSMENT OF COMPLEX SYSTEMS
    (2005-04-20) HU, YUNWEI; MOSLEH, ALI; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Probabilistic risk assessment (PRA) is a systematic process of examining how engineered systems work to ensure safety. With the growth of the size of the dynamic systems and the complexity of the interactions between hardware, software, and humans, it is extremely difficult to enumerate the risky scenarios by the traditional PRA methods. Over the past 15 years, a host of DPRA methods have been proposed to serve as supplemental tools to traditional PRA to deal with complex dynamic systems. A new dynamic probabilistic risk assessment framework is proposed in this dissertation. In this framework a new exploration strategy is employed. The engineering knowledge of the system is explicitly used to guide the simulation to achieve higher efficiency and accuracy. The engineering knowledge is reflected in the "Planner" which is responsible for generating plans as a high level map to guide the simulation. A scheduler is responsible for guiding the simulation by controlling the timing and occurrence of the random events. During the simulation the possible random events are proposed to the scheduler at branch points. The scheduler decides which events are to be simulated. Scheduler would favor the events with higher values. The value of a proposed event depends on the information gain from exploring that scenario, and the importance factor of the scenario. The information gain is measured by the information entropy, and the importance factor is based on the engineering judgment. The simulation results are recorded and grouped for later studies. The planner may "learn" from the simulation results, and update the plan to guide further simulation. SIMPRA is the software package which implements the new methodology. It provides the users with a friendly interface and a rich DPRA library to aid in the construction of the simulation model. The engineering knowledge can be input into the Planner, which would generate a plan automatically. The scheduler would guide the simulation according to the plan. The simulation generates many accident event sequences and estimates of the end state probabilities.