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This paper develops Classical and Bayesian methods for quantifying the uncertainty in reliability for a system of mixed series and parallel components for which both go/no-go and variables data are available. Classical methods focus on uncertainty due to sampling error. Bayesian methods can explore both sampling error and other knowledge-based unce...
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A common approach to learn robotic skills is to imitate a policy demonstrated by a supervisor. One of the existing problems is that, due to the compounding of small errors and perturbations, the robot may leave the states where demonstrations were given. If no strategy is employed to provide a guarantee on how the robot will behave when facing unkn...
Citations
... Among the most important are how to aggregate heterogeneous data in order to estimate health or reliability at the system level, and how to appropriately propagate uncertainty from multiple types of data in a single analysis. Significant progress has been made toward meeting these challenges, and research is ongoing in various application areas; see (Wilson et al., 2006), (Graves et al., 2007Graves et al., , 2008), (Anderson-Cook et al., 2008), (Huzurbazar et al., 2009), (Lorio et al., 2009) for details and examples. ...
Complex systems are increasingly confronted by two conflicting sets of requirements: on the one hand, demands for continuous operational readiness with high reliability and availability; on the other, the need to minimize life cycle cost, implying reduced inspections, maintenance, and logistics support. An emerging paradigm to address this challenge is prognostics and health management (PHM), where measures of system health are used to determine needs for preventive and corrective maintenance, to optimize maintenance scheduling and parts stocking, and to forecast when a system will reach the end of its useful life. Two key components of PHM are a definition of system health and a strategy for how it is to be measured as part of system health assessment (SHA). In this article we discuss system health as a general concept, illustrate its application with examples, and describe how the use of system health metrics as part of an SHA program can facilitate PHM.
We aim to analyze the effects of component level reliability data, including both catastrophic failures and margin failures, on system level reliability. While much work has been done to analyze margins and uncertainties at the component level, a gap exists in relating this component level analysis to the system level. We apply methodologies for aggregating uncertainty from component level data to quantify overall system uncertainty. We explore three approaches towards this goal, the classical Method of Moments (MOM), Bayesian, and Bootstrap methods. These three approaches are used to quantify the uncertainty in reliability for a system of mixed series and parallel components for which both pass/fail and continuous margin data are available. This paper provides proof of concept that uncertainty quantification methods can be constructed and applied to system reliability problems. In addition, application of these methods demonstrates that the results from the three fundamentally different approaches can be quite comparable.
The guest editorial column of the Reliability Engineering and System Safety journal dealt with some of the aspects of the Quantification of Margins and Uncertainties (QMU) procedure of assessing nuclear warheads. The challenge of developing a workable QMU methodology derived from the fact that such a methodology needed to cover a large range of analysis situations. It was found that QMU evolved into an analysis philosophy and an associated assemblage of analysis procedures that had the potential to be adapted to a wide spectrum of analysis situations of differing levels of complexity and computational challenge. Researchers demonstrated that QMU was a scientific methodology that identified relevant nuclear-warhead parameters and quantities, using available experimental and computational tools. It was also found that an assessment of the relationship between the margin and uncertainties facilitated stockpile management decisions and informed judgments on the safety and performance of nuclear warheads.
Key ideas underlying the application of Quantification of Margins and Uncertainties (QMU) to nuclear weapons stockpile lifecycle decisions are described. While QMU is a broad process and methodology for generating critical technical information to be used in U.S. nuclear weapon stockpile management, this paper emphasizes one component, which is information produced by computational modeling and simulation. In particular, the following topics are discussed: (i) the key principles of developing QMU information in the form of Best Estimate Plus Uncertainty, (ii) the need to separate aleatory and epistemic uncertainty in QMU, and (iii) the properties of risk-informed decision making (RIDM) that are best suited for effective application of QMU. The paper is written at a high level, but provides an extensive bibliography of useful papers for interested readers to deepen their understanding of the presented ideas.
