Publications

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The national laboratories global security programs implement sustainable technical solutions for cooperative nonproliferation, arms control, and physical security systems worldwide. To help in the development and execution of these programs, a wide range of analytical tools are used to model, for example, synthetic tactical environments for assessing infrastructure protection initiatives and tactics, systematic approaches for prioritizing nuclear and biological threat reduction opportunities worldwide, and nuclear fuel cycle enrichment and spent fuel management for nuclear power countries. This presentation will describe how these models are used in analyses to support the Obama Administration's agenda and bilateral/multinational treaties, and ultimately, to reduce weapons of mass destruction and terrorism threats through international technical cooperation.

The US wind Industry has experienced remarkable growth since the turn of the century. At the same time, the physical size and electrical generation capabilities of wind turbines has also experienced remarkable growth. As the market continues to expand, and as wind generation continues to gain a significant share of the generation portfolio, the reliability of wind turbine technology becomes increasingly important. This report addresses how operations and maintenance costs are related to unreliability - that is the failures experienced by systems and components. Reliability tools are demonstrated, data needed to understand and catalog failure events is described, and practical wind turbine reliability models are illustrated, including preliminary results. This report also presents a continuing process of how to proceed with controlling industry requirements, needs, and expectations related to Reliability, Availability, Maintainability, and Safety. A simply stated goal of this process is to better understand and to improve the operable reliability of wind turbine installations.

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The Sandia National Laboratories' developed Combined Lifecycle (CMBL) distribution provides an application friendly method for characterizing a component's failure or lifecycle distribution. This paper explores methods for updating the CMBL distribution as new data become available. The initial results obtained in applying Method 1, a Bayesian sequential updating methodology, to the CMBL distribution shows some promise. However, additional research is needed for the updating process involved with the random portion of the curve. Future evaluation of Methods 2 and 3 should also provide greater flexibility in the use of the CMBL distribution and with some generalizing modifications, should apply to other sectional time-to-failure (TTF) failure models. The method developed in this effort for updating the CMBL distribution and other TTF distributions, should be valuable in enhancing maintenance planning and real-time situational awareness processes. This method, used in enterprise level and prognostics and health management (PHM) modeling, should more accurately help provide instant feedback on the current status of equipment; provide tactical assessment of the readiness of equipment for the next campaign; identify parts, services, etc. that are likely to be required during the next campaign; provide a realistic basis for scheduling and optimizing equipment maintenance schedules; and help ensure that the useful life of expensive components is maximized while reducing the incidence of unplanned maintenance. © 2007 IEEE.

Enterprise level logistics and prognostics and health management (PHM) modeling efforts use reliability focused failure distributions to characterize the probability of failure over the lifetime of a component. This research characterized the Sandia National Laboratories developed combined lifecycle (CMBL) distribution and explored methods for updating this distribution as systems age and new failure data becomes available. The initial results obtained in applying a Bayesian sequential updating methodology to the CMBL distribution shows promise. This research also resulted in the development of a closed-form full life cycle (CFLC) distribution similar to the CMBL distribution but with slightly different, yet commonly recognized, input parameters. Further research is warranted to provide additional theoretical validation of the distributions, complete the updating methods for the CMBL distribution, evaluate a Bayesian updating methodology for the CFLC distribution, and determine which updating methods would be most appropriate for enterprise level logistics and PHM modeling.

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