Metallic structures dominated by tensile loads are vulnerable to fatigue and fracture. Fatigue is produced by oscillatory loads. Quasi-static brittle or ductile fracture can result from a "large" load in the random sequence. Moreover, a fatigue or fracture failure in a member of a redundant structure produces impulsive redistributed loads to the intact members. These transient loads could produce a sequence of failures resulting in progressive collapse of the system. Fatigue and fracture design factors are subject to considerable uncertainty. Therefore, a probabilistic approach, which includes a system reliability assessment, is appropriate for design purposes. But system reliability can be improved by a maintenance program of periodic inspection with repair and/or replacement of damaged members. However, a maintenance program can be expensive. The ultimate goal of the engineer is to specify a design, inspection, and repair strategy to minimize life cycle costs. The fatigue/fracture reliability and maintainability (FRM) process for redundant structure can be a complicated random process. The structural model considered series, parallel, and parallel/series systems of elements. Applied to the system are fatigue loads including mean stress, an extreme load, as well as impulsive loads in parallel member systems. The failure modes are fatigue, brittle and ductile fracture. A refined fatigue model is employed which includes both the crack initiation and propagation phases. The FRM process cannot be solved easily using recently developed advanced structural reliability techniques. A "hybrid" simulation method which combines modified importance sampling (MIS) with inflated stress extrapolation (ISE) is proposed. MIS and ISE methods are developed and demonstrated using numerous examples which include series, parallel and series/parallel systems. Not only reasonable estimates of the probability of system failure but also an estimate of the distribution of time to system failure can be obtained. The time to failure distribution can be used to estimate the reliability function, hazard function, conditional reliability given survival at any time, etc. The demonstration cases illustrate how reliability of a system having given material properties is influenced by the number of series and parallel elements, stress level, mean stress, and various inspection/repair policies.

M.Phil. (Engineering Management) / Please refer to full text to view abstract

Approved for public release, distribution is unlimited / The J-52 engine used in the EA-6B Prowler has been found to have a faulty design which has led to in-flight engine failures due to the degradation of the 4.5 roller bearing. Because of cost constraints, the Navy developed a policy of maintaining rather than replacing the faulty engine with a re-designed engine. With an increase in Prowler crashes related to the failure of this bearing, the Navy has begun to re-evaluate this policy. This thesis analyzed the problem using methods in reliability statistics to develop policy recommendations for the Navy. One method analyzed the individual times to failure of the bearings and fit the data to a known distribution. Using this distribution, we estimated lower confidence bounds for the time which 0.0001% of the bearings are expected to fail, finding it was below fifty hours. Such calculations can be used to form maintenance and replacement policies. Another approach analyzed oil samples taken from the J-52 engine. The oil samples contain particles of different metals that compose the 4.5 roller bearing. Linear regression, classification and regression trees, and discriminant analysis were used to determine that molybdenum and vanadium levels are good indicators of when a bearing is near failure. / http://hdl.handle.net/10945/945 / Ensign, United States Navy

Approved for public release; distribution is unlimited / This thesis surveys existing reliability management and improvement techniques, and describes how they can be applied to small unmanned aerial vehicles (SUAVs). These vehicles are currently unreliable, and lack systems to improve their reliability. Selection of those systems, in turn, drives data collection requirements for SUAVs, which we also present, with proposed solutions. This thesis lays the foundation for a Navy-wide SUAV reliability program. / Commander, Hellenic Navy

Electricity demand is rapidly increasing with growth of population, development of technologies and electrically intensive industries. Also, emerging climate change concerns compel governments to seek environmentally friendly ways to produce electricity such as wind energy systems. In 2018, the wind energy reached 600 GW total capacity globally. However, this corresponds to only about 6% of global electricity demand and there is a need to increase wind energy penetration in electricity grids. One way to enhance the competitiveness of wind energy is to improve its reliability and availability and reduce associated maintenance costs. This study utilizes a database entitled “Wind Monitor and Evaluation Program (WMEP)” to investigate, model and improve wind turbine reliability and availability. The WMEP database consists of maintenance data of 575 wind turbines in Germany during 1989-2008. It is unique as it includes details of turbine model and size, affected subsystem and component, cause of failure, date and time of maintenance, location, and energy production from the wind turbines. Additional parameters such as climatic regions, geography number of previous failures and mean annual wind speed are added to the database in this study. In this research, two metrics are considered and developed such as time-to-failure or failure rate and time-to-repair or downtime for reliability and availability, respectively. This study investigated failure causes, effects and criticalities of wind turbine subsystems and components, assessed the risk factors impacting wind turbine reliability, modeled the reliability of wind turbines based on assessed risk factors, and predicted the cost of wind turbine failures under various operational and environmental conditions. A well-established reliability assessment technique - Failure Modes, Effects and Criticality Analysis is applied on the WMEP maintenance data from 109 wind turbines and three different climatic regions to understand the impacts of climate and wind turbine design type on wind turbine reliability and availability. First, climatic region impacts on identical wind turbine failures are investigated, then impacts of wind turbine design type are examined for the same climatic region. Furthermore, we compared the results of this investigation with results from previous FMECA studies which neglected impacts of climatic region and turbine design type in section 5.4. Two-step cluster and survival analyses are used to determine risk factors that affect wind turbine reliability. Six operational and environmental factors are considered for this approach, namely capacity factor (CF), wind turbine design type, number of previous failures (NOPF), geographical location, climatic region and mean annual wind speed (MAWS). Data are classified as frequent (time-to-failure<40 days) and non-frequent (time-to-failure>80 days) failures and we identified 615 operations listing all these factor and energy production from 21 wind turbines in the WMEP data base. These factors are examined for their impact on wind turbine reliability and results are compared. In addition, wind turbine reliability is modeled by machine learning methods, namely logistic regression (LR) and artificial neural network (ANN), using the considered 615 operations. The objective of this investigation is to model and predict probability of frequently-failing wind turbines based on wind turbines’ known operational and environmental conditions. The models are evaluated and cross validated with 10-fold cross validation and prediction performances and compared with other algorithms such as k-nearest neighbor and support vector machines. Also, prediction performances of LR and ANN are discussed along with their easiness to interpret and share with others. Lastly, using data from 753 operations, a decision support tool for predicting cost of wind turbine failures is developed. The tool development includes machine learning application for estimating probability of failures in 60 days of operation and time-to-repair probabilities for divisions of 0-8hrs, 8-16hrs, 16-24hrs and more than 1 day based on operational and environmental conditions of wind turbines. Prediction for cost of wind turbine failures for 60 days of operation is calculated using assumed costs from time-to-repair divisions. The decision support tool can be updated by the user’s discretion on the cost of failures. This study provides a better understanding of wind turbine failures by investigating associated risk factors, modeling wind turbine reliability and predicting the future cost of failures by applying state-of-the art reliability and data analysis techniques. Wind energy developers and operators can be guided by this study in improving the reliability of wind turbines. Also, wind energy investors, operators and maintenance service managers can predict the cost of wind turbine failures with the decision support tool provided in this study.

The need to achieve dependability in critical infrastructures has become indispensable for government and commercial enterprises. This need has become more necessary with the proliferation of malicious attacks on critical systems, such as healthcare, aerospace and airline applications. Additionally, due to the widespread use of web services in critical systems, the need to ensure their reliability is paramount. We believe that patterns can be used to achieve dependability. We conducted a survey of fault tolerance, reliability and web service products and patterns to better understand them. One objective of our survey is to evaluate the state of these patterns, and to investigate which standards are being used in products and their tool support. Our survey found that these patterns are insufficient, and many web services products do not use them. In light of this, we wrote some fault tolerance and web services reliability patterns and present an analysis of them. / by Ingrid A. Buckley. / Thesis (M.S.C.S.)--Florida Atlantic University, 2008. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.

Reliability is a key system characteristic that is an increasing concern for current systems. Greater reliability is necessary due to the new ways in which services are delivered to the public. Services are used by many industries, including health care, government, telecommunications, tools, and products. We have defined an approach to incorporate reliability along the stages of system development. We first did a survey of existing dependability patterns to evaluate their possible use in this methodology. We have defined a systematic methodology that helps the designer apply reliability in all steps of the development life cycle in the form of patterns. A systematic failure enumeration process to define corresponding countermeasures was proposed as a guideline to define where reliability is needed. We introduced the idea of failure patterns which show how failures manifest and propagate in a system. We also looked at how to combine reliability and security. Finally, we defined an approach to certify the level of reliability of an implemented web service. All these steps lead towards a complete methodology. / by Ingrid A. Buckley. / Thesis (Ph.D.)--Florida Atlantic University, 2012. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries / Department: Industrial Engineering.

Industry and society are now aware of risk more than ever before. Organisations whose activities pose risk to individuals and society are accountable to manage and reduce risk to an acceptable level. In this regard, utilisation of Safety Instrumented Systems (SISs) as an independent protection layer is a practical method of achieving the required risk reduction. The role of a SIS is to maintain the safety of equipment under control by providing a safety-related function. The International Standards, IEC 61508/61511, provide a set of guidelines to promote consistency for implementation of SISs used for risk reduction. In accordance with IEC 61508, the performance of a SIS shall be established by computing the associated Probability of Failure on Demand (PFD) as a reliability measure using a suitable technique. The principal purpose of this research is to provide the basis for reliability assessment of redundant SISs affected by process demand as well as component failures. This dissertation introduces four new reliability models for redundant SISs subject to process demand for the first time using Markov analysis technique. The proposed reliability models 1 and 3 incorporates process demands in conjunction with Common Cause Failure (CCF) and evaluates their impacts on the reliability quantification of 1oo2 redundant configuration using different repair philosophies. In model 3, the proposed Markov model was also compared with the IEC 61508 approach for redundant SISs and a reliability improvement between 9% - 15% were observed. The model 2 on the other hand integrates the Dangerous Detected (DD) failure rates in the unavailability assessment of redundant SISs subject to process demand assuming that CCF does not occur. An additional reliability model was developed in this research for a 1oo3 redundant configuration subject to process demand excluding CCF and its construction was verified using partial verification method. Furthermore, a generic framework for reliability assessment of 1oon redundant SISs is provided in this thesis in conjunction with some guidelines for future researchers as how to conduct reliability assessment of SISs subject to process demands. The accuracy of the proposed Markov models is verified for industrial application case studies. It is demonstrated that the proposed approach provides a sufficiently robust result for all demand rates, demand durations, common cause failures, dangerous detected and undetected failure and associated repair rates for SISs. The effectiveness of the proposed models offers a robust opportunity to conduct reliability assessment of redundant SISs subject to process demands.