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Dependability Modelling under Uncertainty: An Imprecise Probabilistic Approach (Studies in Computational Intelligence)
 
 
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Dependability Modelling under Uncertainty: An Imprecise Probabilistic Approach (Studies in Computational Intelligence) [Hardcover]

Philipp Limbourg (Author)

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Book Description

354069286X 978-3540692867 September 26, 2008 1
Mechatronic design processes have become shorter and more parallelized, induced by growing time-to-market pressure. Methods that enable quantitative analysis in early design stages are required, should dependability analyses aim to influence the design. Due to the limited amount of data in this phase, the level of uncertainty is high and explicit modeling of these uncertainties becomes necessary. This work introduces new uncertainty-preserving dependability methods for early design stages. These include the propagation of uncertainty through dependability models, the activation of data from similar components for analyses and the integration of uncertain dependability predictions into an optimization framework. It is shown that Dempster-Shafer theory can be an alternative to probability theory in early design stage dependability predictions. Expert estimates can be represented, input uncertainty is propagated through the system and prediction uncertainty can be measured and interpreted. The resulting coherent methodology can be applied to represent the uncertainty in dependability models.

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Mechatronic design processes have become shorter and more parallelized, induced by growing time-to-market pressure. Methods that enable quantitative analysis in early design stages are required, should dependability analyses aim to influence the design. Due to the limited amount of data in this phase, the level of uncertainty is high and explicit modeling of these uncertainties becomes necessary. This work introduces new uncertainty-preserving dependability methods for early design stages. These include the propagation of uncertainty through dependability models, the activation of data from similar components for analyses and the integration of uncertain dependability predictions into an optimization framework. It is shown that Dempster-Shafer theory can be an alternative to probability theory in early design stage dependability predictions. Expert estimates can be represented, input uncertainty is propagated through the system and prediction uncertainty can be measured and interpreted. The resulting coherent methodology can be applied to represent the uncertainty in dependability models.

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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
similarity estimates, imprecise probabilistic models, dependability prediction, feature set types, output shaft sensor, design space specification, dependability optimization, tacho sensor, target failure measure, redundancy allocation problem, dependability characteristics, imprecise solution, dependability figures, dependability data, weighted mixing, fault forecasting, similarity prediction, dependability models, component failure probabilities, system failure probability, dependability problems, qualifier values, aleatory uncertainty, dominance criterion, optimization progress
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Predicting Dependability Characteristics, Springer-Verlag Berlin Heidelberg, Dependability Modelling, Interval Expert, Point Expert, Monte Carlo, Early Design Stages, Quantifying Reliability According, Illustrative Examples, Multi-Objective Evolutionary Algorithms, Name Cost, The Dempster-Shafer Theory of Evidence
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