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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