Dependability Modelling under Uncertainty

An Imprecise Probabilistic Approach

  • Authors
  • Philipp Limbourg

Part of the Studies in Computational Intelligence book series (SCI, volume 148)

About this book

Introduction

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.

Keywords

Computational Intelligence Dependability Modelling Mechatronics Regression System Uncertainty modeling optimization

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-69287-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-69286-7
  • Online ISBN 978-3-540-69287-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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